LIDAR range scans can be used to quickly create accurate 3D models for virtual reality and as a basis to visualize sets of photographs, videos, and virtual objects in a cohesive environment. The number of existing virtual reality programs that use LIDAR data as input has motivated our group to develop methods for fusing images with 3D scans and for augmenting the scans with both dynamic objects present in videos and virtual models. Bringing together as many data sources as possible increases users' abilities to present related information in one, intuitive venue. We demonstrate how to register a variety of 2D imagery with a range scan to construct photo-realistic models and to extract walking people captured in videos and model them in a 3D space. We also present a method for determining the sun position from a set of stitched photographs in order to apply correct lighting to virtual objects placed amongst real world data. Naturally lit objects can be inserted into original photographs using our 2D-3D registration information. These methods are all combined to display and study photos, videos, and virtual objects in a complete 3D environment.

We propose an approximate shading model for image-based object modeling and insertion. Our approach is a hybrid of 3D rendering and image-based composition. It avoids the difficulties of physically accurate shape estimation from a single image, and allows for more flexible image composition than pure image-based methods. The model decomposes the shading field into (a) a rough shape term that can be reshaded, (b) a parametric shading detail that encodes missing features from the first term, and (c) a geometric detail term that captures fine-scale material properties. With this object model, we build an object relighting system that allows an artist to select an object from an image and insert it into a 3D scene. Through simple interactions, the system can adjust illumination on the inserted object so that it appears more naturally in the scene. Our quantitative evaluation and extensive user study suggest our method is a promising alternative to existing methods of object insertion.

Typically video projectors display images onto white screens, which can result in a washed out image. Projectibles algorithmically control the display surface color to increase the contrast and resolution. By combining a printed image with projected light, we can create animated, high resolution, high dynamic range visual experiences for video sequences. We present two algorithms for separating an input video sequence into a printed component and projected component, maximizing the combined contrast and resolution while minimizing any visual artifacts introduced from the decomposition. We present empirical measurements of real-world results of six example video sequences, subjective viewer feedback ratings, and we discuss the benefits and limitations of Projectibles. This is the first approach to combine a static display with a dynamic display for the display of video, and the first to optimize surface color for projection of video.

We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.

We present a user-friendly image editing system that supports a drag-and-drop object insertion (where the user merely drags objects into the image, and the system automatically places them in 3D and relights them appropriately), postprocess illumination editing, and depth-of-field manipulation. Underlying our system is a fully automatic technique for recovering a comprehensive 3D scene model (geometry, illumination, diffuse albedo, and camera parameters) from a single, low dynamic range photograph. This is made possible by two novel contributions: an illumination inference algorithm that recovers a full lighting model of the scene (including light sources that are not directly visible in the photograph), and a depth estimation algorithm that combines data-driven depth transfer with geometric reasoning about the scene layout. A user study shows that our system produces perceptually convincing results, and achieves the same level of realism as techniques that require significant user interaction.

Early work in computer vision considered a host of geometric cues for both shape reconstruction and recognition. However, since then, the vision community has focused heavily on shading cues for reconstruction, and moved towards data-driven approaches for recognition. In this paper, we reconsider these perhaps overlooked "boundary" cues (such as self occlusions and folds in a surface), as well as many other established constraints for shape reconstruction. In a variety of user studies and quantitative tasks, we evaluate how well these cues inform shape reconstruction (relative to each other) in terms of both shape quality and shape recognition. Our findings suggest many new directions for future research in shape reconstruction, such as automatic boundary cue detection and relaxing assumptions in shape from shading (e.g. orthographic projection, Lambertian surfaces).

Medical image segmentation has become an essential technique in clinical and research-oriented applications. Because manual segmentation methods are tedious, and fully automatic segmentation lacks the flexibility of human intervention or correction, semi-automatic methods have become the preferred type of medical image segmentation. We present a hybrid, semi-automatic segmentation method in 3D that integrates both region-based and boundary-based procedures. Our method differs from previous hybrid methods in that we perform region-based and boundary-based approaches separately, which allows for more efficient segmentation. A region-based technique is used to generate an initial seed contour that roughly represents the boundary of a target brain structure, alleviating the local minima problem in the subsequent model deformation phase. The contour is deformed under a unique force equation independent of image edges. Experiments on MRI data show that this method can achieve high accuracy and efficiency primarily due to the unique seed initialization technique.