ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems

In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces precise depth with a subpixel precision of $1/30th$ of a pixel; it does not suffer from the common over-smoothing issues; it preserves the edges; and it explicitly handles occlusions. We introduce a novel reconstruction loss that is more robust to noise and texture-less patches, and is invariant to illumination changes. The proposed loss is optimized using a window-based cost aggregation with an adaptive support weight scheme. This cost aggregation is edge-preserving and smooths the loss function, which is key to allow the network to reach compelling results. Finally we show how the task of predicting invalid regions, such as occlusions, can be trained end-to-end without ground-truth. This component is crucial to reduce blur and particularly improves predictions along depth discontinuities. Extensive quantitatively and qualitatively evaluations on real and synthetic data demonstrate state of the art results in many challenging scenes.Comment: Accepted by ECCV2018, Oral Presentation, Main paper + Supplementary Material

RAFT: Recurrent All-Pairs Field Transforms for Optical Flow

We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network architecture for optical flow. RAFT extracts per-pixel features, builds multi-scale 4D correlation volumes for all pairs of pixels, and iteratively updates a flow field through a recurrent unit that performs lookups on the correlation volumes. RAFT achieves state-of-the-art performance. On KITTI, RAFT achieves an F1-all error of 5.10%, a 16% error reduction from the best published result (6.10%). On Sintel (final pass), RAFT obtains an end-point-error of 2.855 pixels, a 30% error reduction from the best published result (4.098 pixels). In addition, RAFT has strong cross-dataset generalization as well as high efficiency in inference time, training speed, and parameter count. Code is available at https://github.com/princeton-vl/RAFT.Comment: fixed a formatting issue, Eq 7. no change in conten

Active stereo platform: online epipolar geometry update

This paper presents a novel method to update a variable epipolar geometry platform directly from the motor encoder based on mapping the motor encoder angle to the image space angle, avoiding the use of feature detection algorithms. First, an offline calibration is performed to establish a relationship between the image space and the hardware space. Second, a transformation matrix is generated using the results from this mapping. The transformation matrix uses the updated epipolar geometry of the platform to rectify the images for further processing. The system has an overall error in the projection of ± 5 pixels, which drops to ± 1.24 pixels when the verge angle increases beyond 10°. The platform used in this project has 3° of freedom to control the verge angle and the size of the baseline

Learning Stereo from Single Images

Supervised deep networks are among the best methods for finding correspondences in stereo image pairs. Like all supervised approaches, these networks require ground truth data during training. However, collecting large quantities of accurate dense correspondence data is very challenging. We propose that it is unnecessary to have such a high reliance on ground truth depths or even corresponding stereo pairs. Inspired by recent progress in monocular depth estimation, we generate plausible disparity maps from single images. In turn, we use those flawed disparity maps in a carefully designed pipeline to generate stereo training pairs. Training in this manner makes it possible to convert any collection of single RGB images into stereo training data. This results in a significant reduction in human effort, with no need to collect real depths or to hand-design synthetic data. We can consequently train a stereo matching network from scratch on datasets like COCO, which were previously hard to exploit for stereo. Through extensive experiments we show that our approach outperforms stereo networks trained with standard synthetic datasets, when evaluated on KITTI, ETH3D, and Middlebury.Comment: Accepted as an oral presentation at ECCV 202

Prevalence of sports-related injuries in Paralympic judo: an exploratory study

Objectives The aim was to assess the 1-year retrospective prevalence of athletes reporting a sports-related injury among Paralympic judokas with visual impairment (VI), and to identify any associations between injury, vision class, gender and weight category. Design Cross-sectional retrospective study. Methods The data were collected through an adapted questionnaire given to athletes with VI during an international training camp. Forty-five Paralympic judokas answered the questionnaire. Descriptive statistics and chi-square statistics (p < 0.05) were used to analyse the data. Spearman’s correlation was used to analyse multiple injuries. Results Thirty-eight of the athletes reported an injury, giving a 1-year prevalence of 84% (95% CI 71-93). Male athletes reported significantly more injuries compared to female athletes (p = 0.023). Over two thirds of the injuries (71%; 95% CI 55-83) had a traumatic onset. The majority of injuries (74%; 95% CI 58-85) occurred during judo training, and in the standing technique tachi waza (82%; 95% CI 66-91). The shoulder was the most single affected body location (29%). Forty-five percent of the injuries led to a time loss from sport for more than three weeks, and 40% of judokas reported multiple injuries. Conclusions The results from this study demonstrate a high prevalence of mainly traumatic and severe sports-related injuries amongst athletes with VI participating in Paralympic judo. A first step towards prevention could be to minimize the time in tachi waza. However, to improve sports safety and to develop effective strategies for injury prevention, more comprehensive epidemiological studies, and also technical studies assessing injury mechanisms are warranted

Semi-Dense 3D Reconstruction with a Stereo Event Camera

Event cameras are bio-inspired sensors that offer several advantages, such as low latency, high-speed and high dynamic range, to tackle challenging scenarios in computer vision. This paper presents a solution to the problem of 3D reconstruction from data captured by a stereo event-camera rig moving in a static scene, such as in the context of stereo Simultaneous Localization and Mapping. The proposed method consists of the optimization of an energy function designed to exploit small-baseline spatio-temporal consistency of events triggered across both stereo image planes. To improve the density of the reconstruction and to reduce the uncertainty of the estimation, a probabilistic depth-fusion strategy is also developed. The resulting method has no special requirements on either the motion of the stereo event-camera rig or on prior knowledge about the scene. Experiments demonstrate our method can deal with both texture-rich scenes as well as sparse scenes, outperforming state-of-the-art stereo methods based on event data image representations.Comment: 19 pages, 8 figures, Video: https://youtu.be/Qrnpj2FD1e

Fuzzy Free Path Detection from Disparity Maps by Using Least-Squares Fitting to a Plane

A method to detect obstacle-free paths in real-time which works as part of a cognitive navigation aid system for visually impaired people is proposed. It is based on the analysis of disparity maps obtained from a stereo vision system which is carried by the blind user. The presented detection method consists of a fuzzy logic system that assigns a certainty to be part of a free path to each group of pixels, depending on the parameters of a planar-model fitting. We also present experimental results on different real outdoor scenarios showing that our method is the most reliable in the sense that it minimizes the false positives rate.N. Ortigosa acknowledges the support of Universidad Politecnica de Valencia under grant FPI-UPV 2008 and Spanish Ministry of Science and Innovation under grant MTM2010-15200. S. Morillas acknowledges the support of Universidad Politecnica de Valencia under grant PAID-05-12-SP20120696.Ortigosa Araque, N.; Morillas Gómez, S. (2014). 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3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry:precision maps for ground control and directly georeferenced surveys

Structure-from-motion (SfM) photogrammetry is revolutionising the collection of detailed topographic data, but insight into geomorphological processes is currently restricted by our limited understanding of SfM survey uncertainties. Here, we present an approach that, for the first time, specifically accounts for the spatially variable precision inherent to photo-based surveys, and enables confidence-bounded quantification of 3-D topographic change. The method uses novel 3-D precision maps that describe the 3-D photogrammetric and georeferencing uncertainty, and determines change through an adapted state-of-the-art fully 3-D point-cloud comparison (M3C2; Lague, et al., 2013), which is particularly valuable for complex topography. We introduce this method by: (1) using simulated UAV surveys, processed in photogrammetric software, to illustrate the spatial variability of precision and the relative influences of photogrammetric (e.g. image network geometry, tie point quality) and georeferencing (e.g. control measurement) considerations; (2) we then present a new Monte Carlo procedure for deriving this information using standard SfM software and integrate it into confidence-bounded change detection; before (3) demonstrating geomorphological application in which we use benchmark TLS data for validation and then estimate sediment budgets through differencing annual SfM surveys of an eroding badland. We show how 3-D precision maps enable more probable erosion patterns to be identified than existing analyses, and how a similar overall survey precision could have been achieved with direct survey georeferencing for camera position data with precision half as good as the GCPs’. Where precision is limited by weak georeferencing (e.g. camera positions with multi-metre precision, such as from a consumer UAV), then overall survey precision can scale as n-½ of the control precision (n = number of images). Our method also provides variance-covariance information for all parameters. Thus, we now open the door for SfM practitioners to use the comprehensive analyses that have underpinned rigorous photogrammetric approaches over the last half-century