Stereo matching is a core component in many computer vision and robotics systems. Despite significant advances over the last decade, handling matching ambiguities in ill-posed regions and large disparities remains an open challenge. In this paper, we propose a new deep network architecture, called IGEV++, for stereo matching. The proposed IGEV++ constructs Multi-range Geometry Encoding Volumes (MGEV), which encode coarse-grained geometry information for ill-posed regions and large disparities, while preserving fine-grained geometry information for details and small disparities. To construct MGEV, we introduce an adaptive patch matching module that efficiently and effectively computes matching costs for large disparity ranges and/or ill-posed regions. We further propose a selective geometry feature fusion module to adaptively fuse multi-range and multi-granularity geometry features in MGEV. Then, we input the fused geometry features into ConvGRUs to iteratively update the disparity map. MGEV allows to efficiently handle large disparities and ill-posed regions, such as occlusions and textureless regions, and enjoys rapid convergence during iterations. Our IGEV++ achieves the best performance on the Scene Flow test set across all disparity ranges, up to 768px. Our IGEV++ also achieves state-of-the-art accuracy on the Middlebury, ETH3D, KITTI 2012, and 2015 benchmarks. Specifically, IGEV++ achieves a 3.23% 2-pixel outlier rate (Bad 2.0) on the large disparity benchmark, Middlebury, representing error reductions of 31.9% and 54.8% compared to RAFT-Stereo and GMStereo, respectively. We also present a real-time version of IGEV++ that achieves the best performance among all published real-time methods on the KITTI benchmarks.

Estimating depth from multi-view images captured by a localized monocular camera is an essential task in computer vision and robotics. In this study, we demonstrate that learning a convolutional neural network (CNN) for depth estimation with an auxiliary optical flow network and the epipolar geometry constraint can greatly benefit the depth estimation task and in turn yield large improvements in both accuracy and speed. Our architecture is composed of two tightly-coupled encoder-decoder networks, i.e., an optical flow net and a depth net, the core part being a list of exchange blocks between the two nets and an epipolar feature layer in the optical flow net to improve predictions of both depth and optical flow. Our architecture allows to input arbitrary number of multiview images with a linearly growing time cost for optical flow and depth estimation. Experimental result on five public datasets demonstrates that our method, named DENAO, runs at 38.46fps on a single Nvidia TITAN Xp GPU which is 5.15X ∼ 142X faster than the state-of-the-art depth estimation methods Meanwhile, our DENAO can concurrently output predictions of both depth and optical flow, and performs on par with or outperforms the state-of-the-art depth estimation methods and optical flow methods.

Recent studies employ advanced deep convolutional neural networks (CNNs) for monocular depth perception, which can hardly run efficiently on small drones that rely on low/middle-grade GPU(e.g. TX2 and 1050Ti) for computation. In addition, the methods which can effectively and efficiently produce probabilistic depth prediction with a measure of model confidence have not been well studied. The lack of such a method could yield erroneous, sometimes fatal, decisions in drone applications (e.g. selecting a waypoint in a region with a large depth yet a low estimation confidence). This paper presents a real-time onboard approach for monocular depth prediction and obstacle avoidance with a lightweight probabilistic CNN (pCNN), which will be ideal for use in a lightweight energy-efficient drone. For each video frame, our pCNN can efficiently predict its depth map and the corresponding confidence. The accuracy of our lightweight pCNN is greatly boosted by integrating sparse depth estimation from a visual odometry into the network for guiding dense depth and confidence inference. The estimated depth map is transformed into Ego Dynamic Space (EDS) by embedding both dynamic motion constraints of a drone and the confidence values into the spatial depth map. Traversable waypoints are automatically computed in EDS based on which appropriate control inputs for the drone are produced. Extensive experimental results on public datasets demonstrate that our depth prediction method runs at 12Hz and 45Hz on TX2 and 1050Ti GPU respectively, which is 1.8X~5.6X faster than the state-of-the-art methods and achieves better depth estimation accuracy. We also conducted experiments of obstacle avoidance in both simulated and real environments to demonstrate the superiority of our method to the baseline methods.

Simultaneous localization and mapping (SLAM) using an RGB-D camera is a key enabling technique for many augmented reality (AR) applications. However, most existing RGB-D SLAM methods could fail in dynamic scenarios due to non-trivial pose estimation errors arising from moving objects. In this study, we present an accurate and robust RGB-D SLAM system for dynamic scenarios which can run real-time on a single dual-core CPU. The core of our system is a robust and efficient dynamic keypoint exclusion method which consists of three steps: 1) grouping spatially and appearance related pixels of a keyframe into regions; 2) identifying dynamic regions by checking motion consistency of keypoints in every region; 3) excluding keypoints in the identified dynamic regions as well as the matching points in the 3D local map. The dynamic keypoint exclusion method can be easily integrated into any keypoint based RGB-D SLAM system for improving the accuracy and robustness in dynamic scenes with trivial time increase (16.6ms per frame). Experimental results on the TUM dataset demonstrates that our method which runs on an Intel i7-4900 CPU is even 2.3X faster than the state-of-the-art method DS-SLAM [1] which runs parallel on a P4000 GPU and a comparable CPU. In addition, our system outperforms the state-of-the-art methods [1]–[4] in terms of smaller absolute trajectory errors (ATE). We also apply our system to a real AR application and live experiments with a hand-held RGB-D camera demonstrate the robustness and generalizability of our method in practical scenarios.11A demo video is provided onhttps://github.com/cc-qy/Dynamic-RGB-D-SLAM

In this paper, we present a novel deep learning and direct method based monocular visual odometry system named D2VO. Our system reconstructs the dense depth map of each keyframe and tracks camera poses based on these keyframes. Combining direct method and deep learning, both tracking and mapping of the system could benefit from the geometric measurement and semantic information. For each input frame, a feature pyramid is built and shared by both tracking and mapping process. The depth map of keyframe is efficiently estimated from coarse to fine with the followed multi-view hierarchical depth estimation network. We optimize the camera pose by minimizing photometric error between re-projected features of each frame and its reference keyframe with bundle adjustment. Experimental results on TUM dataset demonstrate that our approach outperforms the state-of-the-art methods on both tracking and mapping.

In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5M frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box, making LaSOT the largest, to the best of our knowledge, densely annotated tracking benchmark. The average video length of LaSOT is more than 2,500 frames, and each sequence comprises various challenges deriving from the wild where target objects may disappear and re-appear again in the view. By releasing LaSOT, we expect to provide the community with a large-scale dedicated benchmark with high quality for both the training of deep trackers and the veritable evaluation of tracking algorithms. Moreover, considering the close connections of visual appearance and natural language, we enrich LaSOT by providing additional language specification, aiming at encouraging the exploration of natural linguistic feature for tracking. A thorough experimental evaluation of 35 tracking algorithms on LaSOT is presented with detailed analysis, and the results demonstrate that there is still a big room for improvements.

Mobile devices equipped with a monocular camera and an inertial measurement unit (IMU) are ideal platforms for augmented reality (AR) applications. However, nontrivial noises in low-cost IMUs, which are usually equipped in consumer-level mobile devices, could lead to large errors in pose estimation and in turn significantly degrade the user experience in mobile AR apps. In this study, we propose a novel monocular visual-inertial state estimation approach for robust and accurate pose estimation even for low-cost IMUs. The core of our method is an IMU pre-integration correction approach which effectively reduces the negative impact of IMU noises using the visual constraints in a sliding window and the kinematic constraint. We seamlessly integrate the IMU pre-integration correction module into a tightly-coupled,sliding-window based optimization framework for state estimation. Experimental results on public dataset EUROC demonstrate the superiority of our method to the state-of-the-art VINS-Mono in terms of smaller absolute trajectory errors (ATE) and relative pose errors (RPE). We further apply our method to real AR applications on two types of consumer-level mobile devices equipped with low-cost IMUs, i.e. an off-the-shelf smartphone and an AR glass. Experimental results demonstrate that our method can facilitate robust AR with little drifts on the two devices.

Considerable advances have been achieved in estimating the depth map from a single image via convolutional neural networks (CNNs) during the past few years. Combining depth prediction from CNNs with conventional monocular simultaneous localization and mapping (SLAM) is promising for accurate and dense monocular reconstruction, in particular addressing the two long-standing challenges in conventional monocular SLAM: low map completeness and scale ambiguity. However, depth estimated by pretrained CNNs usually fails to achieve sufficient accuracy for environments of different types from the training data, which are common for certain applications such as obstacle avoidance of drones in unknown scenes. Additionally, inaccurate depth prediction of CNN could yield large tracking errors in monocular SLAM. In this paper, we present a real-time dense monocular SLAM system, which effectively fuses direct monocular SLAM with an online-adapted depth prediction network for achieving accurate depth prediction of scenes of different types from the training data and providing absolute scale information for tracking and mapping. Specifically, on one hand, tracking pose (i.e., translation and rotation) from direct SLAM is used for selecting a small set of highly effective and reliable training images, which acts as ground truth for tuning the depth prediction network on-the-fly toward better generalization ability for scenes of different types. A stage-wise Stochastic Gradient Descent algorithm with a selective update strategy is introduced for efficient convergence of the tuning process. On the other hand, the dense map produced by the adapted network is applied to address scale ambiguity of direct monocular SLAM which in turn improves the accuracy of both tracking and overall reconstruction. The system with assistance of both CPUs and GPUs, can achieve real-time performance with progressively improved reconstruction accuracy. Experimental results on public datasets and live application to obstacle avoidance of drones demonstrate that our method outperforms the state-of-the-art methods with greater map completeness and accuracy, and a smaller tracking error.

Using deep convolutional neural networks (CNN) to predict the depth from a single image has received considerable attention in recent years due to its impressive performance. However, existing methods process each single image independently without leveraging the multiview information of video sequences in practical scenarios. Properly taking into account multiview information in video sequences beyond individual frames could offer considerable benefits in terms of depth prediction accuracy and robustness. In addition, a meaningful measure of prediction uncertainty is essential for decision making, which is not provided in existing methods. This paper presents a novel video-based depth prediction system based on a monocular camera, named Bayesian DeNet. Specifically, Bayesian DeNet consists of a 59-layer CNN that can concurrently output a depth map and an uncertainty map for each video frame. Each pixel in an uncertainty map indicates the error variance of the corresponding depth estimate. Depth estimates and uncertainties of previous frames are propagated to the current frame based on the tracked camera pose, yielding multiple depth/uncertainty hypotheses for the current frame which are then fused in a Bayesian inference framework for greater accuracy and robustness. Extensive exper-iments on three public datasets demonstrate that our Bayesian DeNet outperforms the state-of-the-art methods for monocular depth prediction. A demo video and code are publicly available.1

Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.

Monocular simultaneous localization and mapping (SLAM) is a key enabling technique for many computer vision and robotics applications. However, existing methods either can obtain only sparse or semi-dense maps in highly-textured image areas or fail to achieve a satisfactory reconstruction accuracy. In this paper, we present a new method based on a generative adversarial network,named DM-GAN, for real-time dense mapping based on a monocular camera. Specifcally, our depth generator network takes a semidense map obtained from motion stereo matching as a guidance to supervise dense depth prediction of a single RGB image. The depth generator is trained based on a combination of two loss functions, i.e. an adversarial loss for enforcing the generated depth maps to reside on the manifold of the true depth maps and a pixel-wise mean square error (MSE) for ensuring the correct absolute depth values. Extensive experiments on three public datasets demonstrate that our DM-GAN signifcantly outperforms the state-of-the-art methods in terms of greater reconstruction accuracy and higher depth completeness.

To exploit the reliable prior knowledge that the target object in tracking must be an object other than nonobject, in this letter, we propose to adapt objectness for visual object tracking. Instead of directly applying an existing objectness measure that is generic and handles various objects and environments, we adapt it to be compatible to the specific tracking sequence and object. More specifically, we use the newly proposed binarized normed gradient (BING) objectness as the base, and then train an object-adaptive objectness for each tracking task. The training is implemented by using an adaptive support vector machine that integrates information from the specific tracking target into the BING measure. We emphasize that the benefit of the proposed adaptive objectness, named ADOBING, is generic. To show this, we combine ADOBING with eight top performed trackers in recent evaluations. We run the ADOBING-enhanced trackers along with their base trackers on the CVPR2013 benchmark, and our methods consistently improve the base trackers both in overall performance and under all challenge factors. Noting that the way we integrate objectness in visual tracking is generic and straightforward, we expect even more improvement by using tracker-specific objectness.

In this paper, we propose a real-time 3D hand pose estimation algorithm using the randomized decision forest framework. Our algorithm takes a depth image as input and generates a set of skeletal joints as output. Previous decision forest-based methods often give labels to all points in a point cloud at a very early stage and vote for the joint locations. By contrast, our algorithm only tracks a set of more flexible virtual landmark points, named segmentation index points (SIPs), before reaching the final decision at a leaf node. Roughly speaking, a SIP represents the centroid of a subset of skeletal joints, which are to be located at the leaves of the branch expanded from the SIP. Inspired by recent latent regression forest-based hand pose estimation framework (Tang et al. 2014), we integrate SIP into the framework with several important improvements: First, we devise a new forest growing strategy, whose decision is made using a randomized feature guided by SIPs. Second, we speed-up the training procedure since only SIPs, not the skeletal joints, are estimated at non-leaf nodes. Third, the experimental results on public benchmark datasets show clearly the advantage of the proposed algorithm over previous state-of-the-art methods, and our algorithm runs at 55.5 fps on a normal CPU without parallelism.

Existing interactive paper systems suffer from the disparate input devices for paper and computers. The finger-pen-only input on paper causes frequent devices switching (e.g. pen vs. mouse) during cross-media interactions, and may have issues of occlusion and precision. We propose MixPad, a novel interactive paper system, which allows users to exploit mice and keyboards to digitally manipulate fine-grained document content on paper, such as copying an arbitrary image region to a computer and clicking on a word for web search. With the combined input channels, MixPad enables richer digital functions on paper and facilitates bimanual operations cross different media. A preliminary user study shows positive feedback on this interaction technique.

Pico projectors have lately been investigated as mobile display and interaction devices. We propose to use them as 'light beams': Everyday objects sojourning in a beam are turned into dedicated projection surfaces and tangible interaction devices. This way, our daily surroundings get populated with interactive objects, each one temporarily chartered with a dedicated sub-issue of pervasive interaction. While interaction with objects has been studied in larger, immersive projection spaces, the affordances of pico projections are fundamentally different: they have a very small, strictly limited field of projection, and they are mobile. This paper contributes the results of an exploratory field study on how people interact with everyday objects in pico projections in nomadic settings. Based upon these results, we present novel interaction techniques that leverage the limited field of projection and trade-off between digitally augmented and traditional uses of everyday objects.

To combine the affordances of paper and computers, prior research has proposed numerous interactive paper systems that link specific paper document content to digital operations such as multimedia playback and proofreading. Yet, it remains unclear to what degree these systems bridge the inherent gap between paper and computers when compared to existing paper-only and computer-only interfaces. In particular, given the special properties of paper, such as limited dynamic feedback, how well does an average new user learn to master the interactive paper system? What factors affect the user performance? And how does the paper interface work in a typical use scenario? To answer these questions, we conducted two empirical experiments on a generic pen-gesture-based command system, called PapierCraft [Liao et al. 2008], for paper-based interfaces. With it, people can select sections of printed document and issue commands such as copy and paste, linking and in-text search. The first experiment focused on the user performance of drawing pen gestures on paper. It proves that users can learn the command system in about 30 minutes and achieve a performance comparable to a table PC-based interface supporting the same gestures. The second experiment examined the application of the command system in active reading tasks. The results show promise for seamless integration of paper and computers in active reading for their combined affordances. In addition, our study identifies some key design issues, such as the pen form factor and feedback of gestures. This article contributes to better understanding on pros and cons of paper and computers, and sheds light on the design of future interfaces for document interaction.

For document visualization, folding techniques provide a focus-plus-context approach with fairly high legibility on flat sections. To enable richer interaction, we explore the design space of multi-touch document folding. We discuss several design considerations for simple modeless gesturing and compatibility with standard Drag and Pinch gestures. We categorize gesture models along the characteristics of Symmetric/Asymmetric and Serial/Parallel, which yields three gesture models. We built a prototype document workspace application that integrates folding and standard gestures, and a system for testing the gesture models. A user study was conducted to compare the three models and to analyze the factors of fold direction, target symmetry, and target tolerance in user performance when folding a document to a specific shape. Our results indicate that all three factors were significant for task times, and parallelism was greater for symmetric targets.

This demo shows an interactive paper system called MixPad, which features using mice and keyboards to enhance the conventional pen-finger-gesture based interaction with paper documents. Similar to many interactive paper systems, MixPad adopts a mobile camera-projector unit to recognize paper documents, detect pen and finger gestures and provide visual feedback. Unlike these systems, MixPad allows using mice and keyboards to help users interact with fine-grained document content on paper (e.g. individual words and user-defined arbitrary regions), and to facilitate cross-media operations. For instance, to copy a document segment from paper to a laptop, one first points a finger of her non-dominant hand to the segment roughly, and then uses a mouse in her dominant hand to refine the selection and drag it to the laptop; she can also type text as a detailed comment on a paper document. This novel interaction paradigm combines the advantages of mice, keyboards, pens and fingers, and therefore enables rich digital functions on paper.

We present a large-scale Embedded Media Marker (EMM) identification system which allows users to retrieve relevant dynamic media associated with a static paper document via camera-phones. The user supplies a query image by capturing an EMM-signified patch of a paper document through a camera phone. The system recognizes the query and in turn retrieves and plays the corresponding media on the phone. Accurate image matching is crucial for positive user experience in this application. To address the challenges posed by large datasets and variation in camera-phone-captured query images, we introduce a novel image matching scheme based on geometrically consistent correspondences. A hierarchical scheme, combined with two constraining methods, is designed to detect geometric constrained correspondences between images. A spatial neighborhood search approach is further proposed to address challenging cases of query images with a large translational shift. Experimental results on a 200k+ dataset show that our solution achieves high accuracy with low memory and time complexity and outperforms the baseline bag-of-words approach. © 2011 ACM.

Embedded Media Markers (EMMs) are nearly transparent icons printed on paper documents that link to associated digital media. By using the document content for retrieval, EMMs are less visually intrusive than barcodes and other glyphs while still providing an indication for the presence of links. An initial implementation demonstrated good overall performance but exposed difficulties in guaranteeing the creation of unambiguous EMMs. We developed an EMM authoring tool that supports the interactive authoring of EMMs via visualizations that show the user which areas on a page may cause recognition errors and automatic feedback that moves the authored EMM away from those areas. The authoring tool and the techniques it relies on have been applied to corpora with different visual characteristics to explore the generality of our approach.

PACER is a gesture-based interactive paper system that supports fine-grained paper document content manipulation through the touch screen of a cameraphone. Using the phone's camera, PACER links a paper document to its digital version based on visual features. It adopts camera-based phone motion detection for embodied gestures (e.g. marquees, underlines and lassos), with which users can flexibly select and interact with document details (e.g. individual words, symbols and pixels). The touch input is incorporated to facilitate target selection at fine granularity, and to address some limitations of the embodied interaction, such as hand jitter and low input sampling rate. This hybrid interaction is coupled with other techniques such as semi-real time document tracking and loose physical-digital document registration, offering a gesture-based command system. We demonstrate the use of PACER in various scenarios including work-related reading, maps and music score playing. A preliminary user study on the design has produced encouraging user feedback, and suggested future research for better understanding of embodied vs. touch interaction and one vs. two handed interaction.

Embedded Media Markers, or simply EMMs, are nearly transparent iconic marks printed on paper documents that signify the existence of media associated with that part of the document. EMMs also guide users' camera operations for media retrieval. Users take a picture of an EMM-signified document patch using a cell phone, and the media associated with the EMM-signified document location is displayed on the phone. Unlike bar codes, EMMs are nearly transparent and thus do not interfere with the document appearance. Retrieval of media associated with an EMM is based on image local features of the captured EMM-signified document patch. This paper describes a technique for semi-automatically placing an EMM at a location in a document, in such a way that it encompasses sufficient identification features with minimal disturbance to the original document.

FACT is an interactive paper system for fine-grained interaction with documents across the boundary between paper and computers. It consists of a small camera-projector unit, a laptop, and ordinary paper documents. With the camera-projector unit pointing to a paper document, the system allows a user to issue pen gestures on the paper document for selecting fine-grained content and applying various digital functions. For example, the user can choose individual words, symbols, figures, and arbitrary regions for keyword search, copy and paste, web search, and remote sharing. FACT thus enables a computer-like user experience on paper. This paper interaction can be integrated with laptop interaction for cross-media manipulations on multiple documents and views. We present the infrastructure, supporting techniques and interaction design, and demonstrate the feasibility via a quantitative experiment. We also propose applications such as document manipulation, map navigation and remote collaboration.

An Embedded Media Marker (EMM) is a transparent mark printed on a paper document that signifies the availability of additional media associated with that part of the document. Users take a picture of the EMM using a camera phone, and the media associated with that part of the document is displayed on the phone. Unlike bar codes, EMMs are nearly transparent and thus do not interfere with the document appearance. Retrieval of media associated with an EMM is based on image features of the document within the EMM boundary. Unlike other feature-based retrieval methods, the EMM clearly indicates to the user the existence and type of media associated with the document location. A semi-automatic authoring tool is used to place an EMM at a location in a document, in such a way that it encompasses sufficient identification features with minimal disturbance to the original document. We will demonstrate how to create an EMM-enhanced document, and how the EMM enables access to the associated media on a cell phone.

This paper presents a tool and a novel Fast Invariant Transform (FIT) algorithm for language independent e-documents access. The tool enables a person to access an e-document through an informal camera capture of a document hardcopy. It can save people from remembering/exploring numerous directories and file names, or even going through many pages/paragraphs in one document. It can also facilitate people's manipulation of a document or people's interactions through documents. Additionally, the algorithm is useful for binding multimedia data to language independent paper documents. Our document recognition algorithm is inspired by the widely known SIFT descriptor [4] but can be computed much more efficiently for both descriptor construction and search. It also uses much less storage space than the SIFT approach. By testing our algorithm with randomly scaled and rotated document pages, we can achieve a 99.73% page recognition rate on the 2188-page ICME06 proceedings and 99.9% page recognition rate on a 504-page Japanese math book [2].

Existing cameraphone-based interactive paper systems fall short of the flexibility of GUIs, partly due to their deficient fine-grained interactions, limited interaction styles and inadequate targeted document types. We present PACER, a platform for applications to interact with document details (e.g. individual words, East Asian characters, math symbols, music notes, and user-specified arbitrary image regions) of generic paper documents through a camera phone. With a see-through phone interface, a user can discover symbol recurrences in a document by pointing the phone's crosshair to a symbol within a printout. The user can also continuously move the phone over a printout for gestures to copy and email an arbitrary region, or play music notes on the printout.

Paper persists as an integral component of active reading and other knowledge-worker tasks because it provides ease of use unmatched by digital alternatives. Paper documents are light to carry, easy to annotate, rapid to navigate, flexible to manipulate, and robust to use in varied environments. Interactions with paper documents create rich webs of annotation, cross reference, and spatial organization. Unfortunately, the resulting webs are confined to the physical world of paper and, as they accumulate, become increasingly difficult to store, search, and access. XLibris [Schilit et al. 1998] and similar systems address these difficulties by simulating paper with tablet PCs. While this approach is promising, it suffers not only from limitations of current tablet computers (e.g., limited screen space) but also from loss of invaluable paper affordances. In this article, we describe PapierCraft, a gesture-based command system that allows users to manipulate digital documents using paper printouts as proxies. Using an Anoto [Anoto 2002] digital pen, users can draw command gestures on paper to tag a paragraph, e-mail a selected area, copy selections to a notepad, or create links to related documents. Upon pen synchronization, PapierCraft executes the commands and presents the results in a digital document viewer. Users can then search the tagged information and navigate the web of annotated digital documents resulting from interactions with the paper proxies. PapierCraft also supports real time interactions across mix-media, for example, letting users copy information from paper to a Tablet PC screen. This article presents the design and implementation of the PapierCraft system and describes user feedback from initial use.

This paper proposes POEMS (paper offered environment management service) that can facilitate the activation of various services with a pen and paper based interface. With this tool, meeting participants can control meeting support devices on the same paper that they take notes. Additionally, a meeting participant can also share his/her paper drawings on a selected public display or initiate a collaborative discussion on a selected public display with a page of paper. Compared with traditional interfaces, such as tablet PC or PDA based interfaces, the interface of this tool has much higher resolution and is much cheaper and easier to deploy. The paper interface is also natural to use for ordinary people. With this tool, the screens are automatically started when a pen is pressed on a piece of paper. A presenter can control his/her presentation progress with time-stamped paper taps or drawings. A meeting participant can use the pen to share writing or make annotations on a public display. Users can use the pen to control pointers on public displays. They can use the pen to control a slideshow playback.

Through a study of field biology practices, we observed that biology fieldwork generates a wealth of heterogeneous information, requiring substantial labor to coordinate and distill. To manage this data, biologists leverage a diverse set of tools, organizing their effort in paper notebooks. These observations motivated ButterflyNet, a mobile capture and access system that integrates paper notes with digital photographs captured during field research. Through ButterflyNet, the activity of leafing through a notebook expands to browsing all associated digital photos. ButterflyNet also facilitates the transfer of captured content to spreadsheets, enabling biologists to share their work. A first-use study with 14 biologists found this system to offer rich data capture and transformation, in a manner felicitous with current practice.

Current paper-based interfaces such as PapierCraft, provide very little feedback and this limits the scope of possible interactions. So far, there has been little systematic exploration of the structure, constraints, and contingencies of feedback-mechanisms in paper-based interaction systems for paper-only environments. We identify three levels of feedback: discovery feedback (e.g., to aid with menu learning), status-indication feedback (e.g., for error detection), and task feedback (e.g., to aid in a search task). Using three modalities (visual, tactile, and auditory) which can be easily implemented on a pen-sized computer, we introduce a conceptual matrix to guide systematic research on pen-top feedback for paper-based interfaces. Using this matrix, we implemented a multimodal pen prototype demonstrating the potential of our approach. We conducted an experiment that confirmed the efficacy of our design in helping users discover a new interface and identify and correct their errors.

Knowledge workers use paper extensively for document reviewing and note-taking due to its versatility and simplicity of use. As users annotate printed documents and gather notes, they create a rich web of annotations and cross references. Unfortunately, as paper is a static media, this web often gets trapped in the physical world. While several digital solutions such as XLibris [15] and Digital Desk [18] have been proposed, they suffer from a small display size or onerous hardware requirements.To address these limitations, we propose PapierCraft, a gesture-based interface that allows users to manipulate digital documents directly using their printouts as proxies. Using a digital pen, users can annotate a printout or draw command gestures to indicate operations such as copying a document area, pasting an area previously copied, or creating a link. Upon pen synchronization, our infrastructure executes these commands and presents the result in a customized viewer. In this paper we describe the design and implementation of the PapierCraft command system, and report on early user feedback.

Using a machine to assist remote environment management can save people's time, effort, and traveling cost. This paper proposes a trainable mobile robot system, which allows people to watch a remote site through a set of cameras installed on the robot, drive the platform around, and control remote devices using mouse or pen based gestures performed in video windows. Furthermore, the robot can learn device operations when it is being used by humans. After being used for a while, the robot can automatically select device control interfaces, or launch a pre-defined operation sequence based on its sensory inputs.

Many conference rooms are now equipped with multiple multi-media devices, such as plasma displays and surrounding speakers, to enhance presentation quality. However, most existing presentation authoring tools are based on the one-display-and-one-speaker assumption, which makes it difficult to organize and playback a presentation dispatched to multiple devices, thus hinders users from taking full advantage of additional multimedia devices. In this paper, we propose and implement a tool to facilitate authoring and playback of a multi-channel presentation in a media devices distributed environment. The tool, named PreAuthor, provides an intuitive and visual way to author a multi-channel presentation by dragging and dropping "hyper-slides" on corresponding visual representations of various devices. PreAuthor supports "hyper-slide" synchronization among various output devices during preview and playback. It also offers multiple options for the presenter to view the presentation in a rendered image sequence, live video, 3D VRML model, or real environment.

Vehicles are part of people's life in modern society, into which more and more high-tech devices are integrated, and a common platform for inter-vehicle communication is necessary to realize an intelligent transportation system supporting safe driving, dynamic route scheduling, emergency message dissemination, and traffic condition monitoring. TrafficView, which is a part of the e-Road project, defines a framework to disseminate and gather information about the vehicles on the road. Using such a system will provide a vehicle driver with road traffic information, which helps driving in situations such as foggy weather, or finding an optimal route in a trip several miles long. This paper describes the basic design of TrafficView and different algorithms used in the system.

This paper presents an information-driven audiovisual signal acquisition approach. This approach has several advantages: users are encouraged to assist in signal acquisition; available sensors are managed based on both signal characteristics and users' suggestions. The problem formulation is consistent with many well-known empirical approaches widely used in previous systems and may provide analytical explanations to these approaches. We demonstrate the use of this approach to pan/tilt/zoom (PTZ) camera management with field data.

We present a system that allows remote and local participants to control devices in a meeting environment using mouse or pen based gestures "through" video windows. Unlike state-of-the-art device control interfaces that require interaction with text commands, buttons, or other artificial symbols, our approach allows users to interact with devices through live video of the environment. This naturally extends our video supported pan/tilt/zoom (PTZ) camera control system, by allowing gestures in video windows to control not only PTZ cameras, but also other devices visible in video images. For example, an authorized meeting participant can show a presentation on a screen by dragging the file on a personal laptop and dropping it on the video image of the presentation screen. This paper presents the system architecture, implementation tradeoffs, and various meeting control scenarios.