Obtaining a good baseline between different video frames is one of the key elements in vision-based monocular SLAM systems. However, if the video frames contain only a few 2D feature correspondences with a good baseline, or the camera only rotates without sufficient translation in the beginning, tracking and mapping becomes unstable. We introduce a real-time visual SLAM system that incrementally tracks individual 2D features, and estimates camera pose by using matched 2D features, regardless of the length of the baseline.

Conventional pipelines for capturing, displaying, and storing images are usually defined as a series of cascaded modules, each responsible for addressing a particular problem. While this divide-and-conquer approach offers many benefits, it also introduces a cumulative error, as each step in the pipeline only considers the output of the previous step, not the original sensor data.

We present a method for video stabilization and rolling-shutter correction for videos captured on mobile devices. The method uses the data from an on-board gyroscope to track the camera's angular velocity, and can run in real time within the camera capture pipeline.

Deep Geometry Buffers (G-buffers) combine the fine-scale and efficiency of screen-space data with much of the robustness of voxels. We introduce a new hardware-aware method for computing two-layer deep G-buffers and show how to produce dynamic indirect radiosity, ambient occlusion (AO), and mirror reflection from them in real-time. Our illumination computation approaches the performance of today’s screen-space AO-only rendering passes on current GPUs and far exceeds their quality.

Collaborative filtering collects similar patches, jointly filters them, and scatters the output back to input patches; each pixel gets a contribution from each patch that overlaps with it, allowing signal reconstruction from highly corrupted data. Exploiting self-similarity, however, requires finding matching image patches, which is an expensive operation. We propose a GPU-friendly approximated-nearest-neighbor algorithm that produces high-quality results for any type of collaborative filter. We evaluate our ANN search against state-of-the-art ANN algorithms in several application domains.

Since its invention, photography has been driven by a relatively fixed paradigm: capture, develop, and print.

Even with the advent of digital photography, the photographic process still continues to focus on creating a single, final still image suitable for printing. This implicit association between a display pixel and a static RGB value can constrain a photographer's creative agency.



We present dynamic image stacks, an interactive image viewer exploring what photography can become when this constraint is relaxed.

During the performance optimization of a computer vision system, developers frequently run into platform-level inefficiencies and bottlenecks that can not be addressed by traditional methods. OpenVX is designed to address such system-level issues by means of a graph-based computation model. This approach differs from the traditional acceleration of one-off functions, and exposes optimization possibilities that might not be available or obvious with traditional computer vision libraries such as OpenCV.

We demonstrate that layered spatial light modulators (SLMs), subject to fixed lateral displacements and refreshed at staggered intervals, can synthesize images with greater spatiotemporal resolution than that afforded by any single SLM used in their construction. Dubbed cascaded displays, such architectures enable superresolution flat panel displays (e.g., using thin stacks of liquid crystal displays (LCDs)) and digital projectors (e.g., relaying the image of one SLM onto another).

Conventional depth video compression uses video codecs designed for color images. Given the performance of current encoding standards, this solution seems efficient. However, such an approach suffers from many issues stemming from discrepancies between depth and light perception. To exploit the inherent limitations of human depth perception, we propose a novel depth compression method that employs a disparity perception model. In contrast to previous methods, we account for disparity masking, and model a distinct relation between depth perception and contrast in luminance.

Digital cameras with electronic viewfinders provide a relatively faithful depiction of the final image, providing a WYSIWYG experience. If, however, the image is created from a burst of differently captured images, or non-linear interactive edits significantly alter the final outcome, then the photographer cannot directly see the results, but instead must imagine the post-processing effects. This paper explores the notion of viewfinder editing, which makes the viewfinder more accurately reflect the final image the user intends to create.