Scene Flow Estimation
64 papers with code • 5 benchmarks • 7 datasets
Optical flow is a two-dimensional motion field in the image plane. It is the projection of the three-dimensional motion of the world. If the world is completely non-rigid, the motions of the points in the scene may all be indepen- dent of each other. One representation of the scene motion is therefore a dense three-dimensional vector field defined for every point on every surface in the scene. By analogy with optical flow, we refer to this three-dimensional motion field as scene flow.
Source: Vedula, Sundar, et al. "Three-dimensional scene flow." IEEE transactions on pattern analysis and machine intelligence 27.3 (2005): 475-480. pdf
Benchmarks
These leaderboards are used to track progress in Scene Flow Estimation
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KITTI
FlyingThings3D
Argoverse 2
DeformingThings4D
Spring
Middlebury 2003
EKubric
Most implemented papers
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation
FilippoAleotti/Dwarf-Tensorflow
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ECCV 2018
Making use of the estimated occlusions, we also show improved results on motion segmentation and scene flow estimation.
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding
chenxuluo/EPC
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Performance on the five tasks of depth estimation, optical flow estimation, odometry, moving object segmentation and scene flow estimation shows that our approach outperforms other SoTA methods.
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation
dfki-av/pwoc-3d
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In the last few years, convolutional neural networks (CNNs) have demonstrated increasing success at learning many computer vision tasks including dense estimation problems such as optical flow and stereo matching.
A Conditional Adversarial Network for Scene Flow Estimation
The problem of Scene flow estimation in depth videos has been attracting attention of researchers of robot vision, due to its potential application in various areas of robotics.
SENSE: a Shared Encoder Network for Scene-flow Estimation
We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion estimation, and semantic segmentation.
PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion.
Just Go with the Flow: Self-Supervised Scene Flow Estimation
HimangiM/Just-Go-with-the-Flow-Self-Supervised-Scene-Flow-Estimation
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CVPR 2020
When interacting with highly dynamic environments, scene flow allows autonomous systems to reason about the non-rigid motion of multiple independent objects.
Self-Supervised Monocular Scene Flow Estimation
visinf/self-mono-sf
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CVPR 2020
Our model achieves state-of-the-art accuracy among unsupervised/self-supervised learning approaches to monocular scene flow, and yields competitive results for the optical flow and monocular depth estimation sub-tasks.
AANet: Adaptive Aggregation Network for Efficient Stereo Matching
Despite the remarkable progress made by learning based stereo matching algorithms, one key challenge remains unsolved.
LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation
The keys to success lie in the use of cost volume and coarse-to-fine flow inference.
