13,564 research outputs found
Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios
This work introduces an evaluation benchmark for depth estimation and
completion using high-resolution depth measurements with angular resolution of
up to 25" (arcsecond), akin to a 50 megapixel camera with per-pixel depth
available. Existing datasets, such as the KITTI benchmark, provide only sparse
reference measurements with an order of magnitude lower angular resolution -
these sparse measurements are treated as ground truth by existing depth
estimation methods. We propose an evaluation methodology in four characteristic
automotive scenarios recorded in varying weather conditions (day, night, fog,
rain). As a result, our benchmark allows us to evaluate the robustness of depth
sensing methods in adverse weather and different driving conditions. Using the
proposed evaluation data, we demonstrate that current stereo approaches provide
significantly more stable depth estimates than monocular methods and lidar
completion in adverse weather. Data and code are available at
https://github.com/gruberto/PixelAccurateDepthBenchmark.git.Comment: 3DV 201
Simultaneous Stereo Video Deblurring and Scene Flow Estimation
Videos for outdoor scene often show unpleasant blur effects due to the large
relative motion between the camera and the dynamic objects and large depth
variations. Existing works typically focus monocular video deblurring. In this
paper, we propose a novel approach to deblurring from stereo videos. In
particular, we exploit the piece-wise planar assumption about the scene and
leverage the scene flow information to deblur the image. Unlike the existing
approach [31] which used a pre-computed scene flow, we propose a single
framework to jointly estimate the scene flow and deblur the image, where the
motion cues from scene flow estimation and blur information could reinforce
each other, and produce superior results than the conventional scene flow
estimation or stereo deblurring methods. We evaluate our method extensively on
two available datasets and achieve significant improvement in flow estimation
and removing the blur effect over the state-of-the-art methods.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
Combining Stereo Disparity and Optical Flow for Basic Scene Flow
Scene flow is a description of real world motion in 3D that contains more
information than optical flow. Because of its complexity there exists no
applicable variant for real-time scene flow estimation in an automotive or
commercial vehicle context that is sufficiently robust and accurate. Therefore,
many applications estimate the 2D optical flow instead. In this paper, we
examine the combination of top-performing state-of-the-art optical flow and
stereo disparity algorithms in order to achieve a basic scene flow. On the
public KITTI Scene Flow Benchmark we demonstrate the reasonable accuracy of the
combination approach and show its speed in computation.Comment: Commercial Vehicle Technology Symposium (CVTS), 201
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.Comment: The project web page at
http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the
paper with high-resolution images as well as additional materia
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