Improved depth recovery in consumer depth cameras via disparity space fusion within cross-spectral stereo.

We address the issue of improving depth coverage in consumer depth cameras based on the combined use of cross-spectral stereo and near infra-red structured light sensing. Specifically we show that fusion of disparity over these modalities, within the disparity space image, prior to disparity optimization facilitates the recovery of scene depth information in regions where structured light sensing fails. We show that this joint approach, leveraging disparity information from both structured light and cross-spectral sensing, facilitates the joint recovery of global scene depth comprising both texture-less object depth, where conventional stereo otherwise fails, and highly reflective object depth, where structured light (and similar) active sensing commonly fails. The proposed solution is illustrated using dense gradient feature matching and shown to outperform prior approaches that use late-stage fused cross-spectral stereo depth as a facet of improved sensing for consumer depth cameras

R3^3SGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems

Stereo depth estimation is used for many computer vision applications. Though many popular methods strive solely for depth quality, for real-time mobile applications (e.g. prosthetic glasses or micro-UAVs), speed and power efficiency are equally, if not more, important. Many real-world systems rely on Semi-Global Matching (SGM) to achieve a good accuracy vs. speed balance, but power efficiency is hard to achieve with conventional hardware, making the use of embedded devices such as FPGAs attractive for low-power applications. However, the full SGM algorithm is ill-suited to deployment on FPGAs, and so most FPGA variants of it are partial, at the expense of accuracy. In a non-FPGA context, the accuracy of SGM has been improved by More Global Matching (MGM), which also helps tackle the streaking artifacts that afflict SGM. In this paper, we propose a novel, resource-efficient method that is inspired by MGM's techniques for improving depth quality, but which can be implemented to run in real time on a low-power FPGA. Through evaluation on multiple datasets (KITTI and Middlebury), we show that in comparison to other real-time capable stereo approaches, we can achieve a state-of-the-art balance between accuracy, power efficiency and speed, making our approach highly desirable for use in real-time systems with limited power.Comment: Accepted in FPT 2018 as Oral presentation, 8 pages, 6 figures, 4 table

Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices

For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts. Their biggest drawback, however, is that they do not directly sense depth maps; instead, these must be estimated through data-intensive processes. Therefore, appropriate algorithm selection plays an important role in achieving the desired performance characteristics. Motivated by applications in space and mobile robotics, we implement and evaluate a FPGA-accelerated adaptation of the ELAS algorithm. Despite offering one of the best trade-offs between efficiency and accuracy, ELAS has only been shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all intriguing properties of the original algorithm, such as the slanted plane priors, but can achieve a frame rate of 47fps whilst consuming under 4W of power. Unlike previous FPGA based designs, we take advantage of both components on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate more complex and computationally diverse algorithms for such low power, real-time systems.Comment: 8 pages, 7 figures, 2 table

Stereo image analysis using connected operators

Connected operators are increasingly used in image processing due to their properties of simplifying the image with various criteria, without loosing contour's information. These properties are related to the connected operator approach that either preserves or completely eliminates a determined connected component, according to an established criterion of analysis. In this paper we will define a new connected operator for stereo images. The goal is to simplify one of the images (left) in the sense that the operator will eliminate the image components that are not present at a determined location in the other image (right). This filter let us select in a stereo image, objects as a function of their distance from the observer (for instance used in auto guided vehicles).Peer ReviewedPostprint (published version

Improved Depth Map Estimation from Stereo Images based on Hybrid Method

In this paper, a stereo matching algorithm based on image segments is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and Mean Shift algorithms with aim to refine the disparity and depth map by using a stereo pair of images. This algorithm utilizes image filtering and modified SAD (Sum of Absolute Differences) stereo matching method. Firstly, a color based segmentation method is applied for segmenting the left image of the input stereo pair (reference image) into regions. The aim of the segmentation is to simplify representation of the image into the form that is easier to analyze and is able to locate objects in images. Secondly, results of the segmentation are used as an input of the local window-based matching method to determine the disparity estimate of each image pixel. The obtained experimental results demonstrate that the final depth map can be obtained by application of segment disparities to the original images. Experimental results with the stereo testing images show that our proposed Hybrid algorithm HSAD gives a good performance

Cortical Dynamics of 3-D Surface Perception: Binocular and Half-Occluded Scenic Images

Previous models of stereopsis have concentrated on the task of binocularly matching left and right eye primitives uniquely. A disparity smoothness constraint is often invoked to limit the number of possible matches. These approaches neglect the fact that surface discontinuities are both abundant in natural everyday scenes, and provide a useful cue for scene segmentation. da Vinci stereopsis refers to the more general problem of dealing with surface discontinuities and their associated unmatched monocular regions within binocular scenes. This study develops a mathematical realization of a neural network theory of biological vision, called FACADE Theory, that shows how early cortical stereopsis processes are related to later cortical processes of 3-D surface representation. The mathematical model demonstrates through computer simulation how the visual cortex may generate 3-D boundary segmentations and use them to control filling-in of 3-D surface properties in response to visual scenes. Model mechanisms correctly match disparate binocular regions while filling-in monocular regions with the correct depth within a binocularly viewed scene. This achievement required introduction of a new multiscale binocular filter for stereo matching which clarifies how cortical complex cells match image contours of like contrast polarity, while pooling signals from opposite contrast polarities. Competitive interactions among filter cells suggest how false binocular matches and unmatched monocular cues, which contain eye-of-origin information, arc automatically handled across multiple spatial scales. This network also helps to explain data concerning context-sensitive binocular matching. Pooling of signals from even-symmetric and odd-symmctric simple cells at complex cells helps to eliminate spurious activity peaks in matchable signals. Later stages of cortical processing by the blob and interblob streams, including refined concepts of cooperative boundary grouping and reciprocal stream interactions between boundary and surface representations, arc modeled to provide a complete simulation of the da Vinci stereopsis percept.Office of Naval Research (N00014-95-I-0409, N00014-85-1-0657, N00014-92-J-4015, N00014-91-J-4100); Airforce Office of Scientific Research (90-0175); National Science Foundation (IRI-90-00530); The James S. McDonnell Foundation (94-40

MISR stereoscopic image matchers: techniques and results

The Multi-angle Imaging SpectroRadiometer (MISR) instrument, launched in December 1999 on the NASA EOS Terra satellite, produces images in the red band at 275-m resolution, over a swath width of 360 km, for the nine camera angles 70.5/spl deg/, 60/spl deg/, 45.6/spl deg/, and 26.1/spl deg/ forward, nadir, and 26.1/spl deg/, 45.6/spl deg/, 60/spl deg/, and 70.5/spl deg/ aft. A set of accurate and fast algorithms was developed for automated stereo matching of cloud features to obtain cloud-top height and motion over the nominal six-year lifetime of the mission. Accuracy and speed requirements necessitated the use of a combination of area-based and feature-based stereo-matchers with only pixel-level acuity. Feature-based techniques are used for cloud motion retrieval with the off-nadir MISR camera views, and the motion is then used to provide a correction to the disparities used to measure cloud-top heights which are derived from the innermost three cameras. Intercomparison with a previously developed "superstereo" matcher shows that the results are very comparable in accuracy with much greater coverage and at ten times the speed. Intercomparison of feature-based and area-based techniques shows that the feature-based techniques are comparable in accuracy at a factor of eight times the speed. An assessment of the accuracy of the area-based matcher for cloud-free scenes demonstrates the accuracy and completeness of the stereo-matcher. This trade-off has resulted in the loss of a reliable quality metric to predict accuracy and a slightly high blunder rate. Examples are shown of the application of the MISR stereo-matchers on several difficult scenes which demonstrate the efficacy of the matching approach