2,763 research outputs found
High-Performance and Tunable Stereo Reconstruction
Traditional stereo algorithms have focused their efforts on reconstruction
quality and have largely avoided prioritizing for run time performance. Robots,
on the other hand, require quick maneuverability and effective computation to
observe its immediate environment and perform tasks within it. In this work, we
propose a high-performance and tunable stereo disparity estimation method, with
a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can
potentially enable robots to quickly reconstruct their immediate surroundings
and maneuver at high-speeds. Our key contribution is a disparity estimation
algorithm that iteratively approximates the scene depth via a piece-wise planar
mesh from stereo imagery, with a fast depth validation step for semi-dense
reconstruction. The mesh is initially seeded with sparsely matched keypoints,
and is recursively tessellated and refined as needed (via a resampling stage),
to provide the desired stereo disparity accuracy. The inherent simplicity and
speed of our approach, with the ability to tune it to a desired reconstruction
quality and runtime performance makes it a compelling solution for applications
in high-speed vehicles.Comment: Accepted to International Conference on Robotics and Automation
(ICRA) 2016; 8 pages, 5 figure
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
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
Entropy-difference based stereo error detection
Stereo depth estimation is error-prone; hence, effective error detection
methods are desirable. Most such existing methods depend on characteristics of
the stereo matching cost curve, making them unduly dependent on functional
details of the matching algorithm. As a remedy, we propose a novel error
detection approach based solely on the input image and its depth map. Our
assumption is that, entropy of any point on an image will be significantly
higher than the entropy of its corresponding point on the image's depth map. In
this paper, we propose a confidence measure, Entropy-Difference (ED) for stereo
depth estimates and a binary classification method to identify incorrect
depths. Experiments on the Middlebury dataset show the effectiveness of our
method. Our proposed stereo confidence measure outperforms 17 existing measures
in all aspects except occlusion detection. Established metrics such as
precision, accuracy, recall, and area-under-curve are used to demonstrate the
effectiveness of our method
Anytime Stereo Image Depth Estimation on Mobile Devices
Many applications of stereo depth estimation in robotics require the
generation of accurate disparity maps in real time under significant
computational constraints. Current state-of-the-art algorithms force a choice
between either generating accurate mappings at a slow pace, or quickly
generating inaccurate ones, and additionally these methods typically require
far too many parameters to be usable on power- or memory-constrained devices.
Motivated by these shortcomings, we propose a novel approach for disparity
prediction in the anytime setting. In contrast to prior work, our end-to-end
learned approach can trade off computation and accuracy at inference time.
Depth estimation is performed in stages, during which the model can be queried
at any time to output its current best estimate. Our final model can process
1242375 resolution images within a range of 10-35 FPS on an NVIDIA
Jetson TX2 module with only marginal increases in error -- using two orders of
magnitude fewer parameters than the most competitive baseline. The source code
is available at https://github.com/mileyan/AnyNet .Comment: Accepted by ICRA201
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