Efficient 3D space sampling to represent an underlying 3D object/scene is essential for 3D vision, robotics, and beyond. A standard approach is to explicitly sample a dense collection of views and formulate it as a view selection problem, or, more generally, a set cover problem. In this paper, we introduce a novel approach that avoids dense view sampling. The key idea is to learn a view prediction network and a trainable aggregation module that takes the predicted views as input and outputs an approximation of their generic scores (e.g., surface coverage, viewing angle from surface normals). This methodology allows us to turn the set cover problem (or multi-view representation optimization) into a continuous optimization problem. We then explain how to effectively solve the induced optimization problem using continuation, i.e., aggregating a hierarchy of smoothed scoring modules. Experimental results show that our approach arrives at similar or better solutions with about 10 x speed up in running time, comparing with the standard methods.

We propose a novel sensing technique called proactive sensing. Proactive sensing continually repositions a camera-based sensor as a way to improve hand pose estimation. Our core contribution is a scheme that effectively learns how to move the sensor to improve pose estimation confidence while requiring no ground truth hand poses. We demonstrate this concept using a low-cost rapid swing arm system built around the state-of-the-art commercial sensing system Leap Motion. The results from our user study show that proactive sensing helps estimate users' hand poses with higher confidence compared to both static and random sensing. We further present an online model update to improve performance for each user.

Large-scale, ground-level urban imagery has recently developed as an important element of online mapping tools such as Google's Street View. Such imagery is extremely valuable in a number of potential applications, ranging from augmented reality to 3D modeling, and from urban planning to monitoring city infrastructure. While such imagery is already available from many sources, including Street View and tourist photos on photo-sharing sites, these collections have drawbacks related to high cost, incompleteness, and accuracy. A potential solution is to leverage the community of photographers around the world to collaboratively acquire large-scale image collections. This work explores this approach through PhotoCity, an online game that trains its players to become "experts" at taking photos at targeted locations and in great density, for the purposes of creating 3D building models. To evaluate our approach, we ran a competition between two universities that resulted in the submission of over 100,000 photos, many of which were highly relevant for the 3D modeling task at hand. Although the number of players was small, we found that this was compensated for by incentives that drove players to become experts at photo collection, often capturing thousands of useful photos each.

We are interested in reconstructing real world locations as detailed 3D models, but to achieve this goal, we require a large quantity of photographic data. We designed a game to employ the efforts and digital cameras of everyday people to not only collect this data, but to do so in a fun and effective way. The result is PhotoCity, a game played outdoors with a camera, in which players take photos to capture flags and take over virtual models of real buildings. The game falls into the genres of both games with a purpose (GWAPs) and alternate reality games (ARGs). Each type of game comes with its own inherent challenges, but as a hybrid of both, PhotoCity presented us with a unique combination of obstacles. This paper describes the design decisions made to address these obstacles, and seeks to answer the question: Can games be used to achieve massive data-acquisition tasks when played in the real world, away from standard game consoles? We conclude with a report on player experiences and showcase some 3D reconstructions built by players during gameplay.

In this paper, we propose an effective scheme to enhance the visual details at the minimal cost of user adjustments. The uprising importance of automatic tone reproduction comes from the increasing population of digital archive programs, which contains a large number of images/videos either old, irreproducible, or poorly captured. We attempt to solve above issues by a new local normalization step and an adaptive contrast assessment process. With those two processes, our method can effectively enhance poor quality regions and simultaneously preserving good quality ones with default parameter settings. The experimental results demonstrate that our method is superior to many existing algorithms when applied to aid digital archiving issues.

Producing an appropriate extent of visually salient regions in video sequences is a challenging task. In this work, we propose a novel approach for modeling dynamic visual attention based on spatiotemporal analysis. Our model first detects salient points in three-dimensional video volumes, and then uses them as seeds to search the extent of salient regions in a motion attention map. To determine the extent of attended regions, the maximum entropy in the spatial domain is used to analyze the dynamics obtained from spatiotemporal analysis. The experiment results show that the proposed dynamic visual attention model can effectively detect visual saliency through successive video volumes.

In this paper, we propose an automatic tone reproduction scheme. This scheme enhances the visual details of an image’s low contrast regions, while preserving the details of high contrast regions. Our method first segments an image into a number of clusters based on luminance information. Subsequently, our scheme calculates the local variation of each cluster. Using the set of computed local variations, we obtain an adaptive gain factor of each pixel to make details of the image visible. With this adaptive gain factor, our method can directly enhance low contrast regions without affecting high contrast ones. The experimental results demonstrate that our method is superior to many existing algorithms, especially when testing on images that contain both high and low contrast regions. The reason behind these results is clear: most tone reproduction algorithms recover the details of low contrast regions, but simultaneously suppress the high contrast regions. Our algorithm, however, recovers the visual details of low contrast regions without sacrificing the quality of high contrast regions. Most importantly, our proposed automatic tone reproduction scheme eliminates the effort of manual adjustment of parameters.