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Found 9 papers, 9 papers with code
Deep Patch Visual Odometry
DPVO disproves this assumption, showing that it is possible to get the best accuracy and efficiency by exploiting the advantages of sparse patch-based matching over dense flow.
Multiview Stereo with Cascaded Epipolar RAFT
CER-MVS is significantly different from prior work in multiview stereo.
Coupled Iterative Refinement for 6D Multi-Object Pose Estimation
We propose a new approach to 6D object pose estimation which consists of an end-to-end differentiable architecture that makes use of geometric knowledge.
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching
We introduce RAFT-Stereo, a new deep architecture for rectified stereo based on the optical flow network RAFT.
Ranked #1 on
Stereo Disparity Estimation
on Middlebury 2014
DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
We introduce DROID-SLAM, a new deep learning based SLAM system.
Tangent Space Backpropagation for 3D Transformation Groups
We address the problem of performing backpropagation for computation graphs involving 3D transformation groups SO(3), SE(3), and Sim(3).
RAFT-3D: Scene Flow using Rigid-Motion Embeddings
We address the problem of scene flow: given a pair of stereo or RGB-D video frames, estimate pixelwise 3D motion.
Ranked #2 on
Scene Flow Estimation
on Spring
RAFT: Recurrent All-Pairs Field Transforms 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.
Ranked #6 on
Optical Flow Estimation
on Spring
DeepV2D: Video to Depth with Differentiable Structure from Motion
We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video.
