234 citations · 588 across the 10 of their papers we have counts for
10 papers · 1 filter
Deep Patch Visual SLAM
Lahav Lipson, Zachary Teed, Jia Deng
Recent work in visual SLAM has shown the effectiveness of using deep network backbones. Despite excellent accuracy, however, such approaches are often expensive to run or do not ge…
Deep Patch Visual Odometry
Zachary Teed, Lahav Lipson, Jia Deng
We propose Deep Patch Visual Odometry (DPVO), a new deep learning system for monocular Visual Odometry (VO). DPVO uses a novel recurrent network architecture designed for tracking…
Multiview Stereo with Cascaded Epipolar RAFT
Zeyu Ma, Zachary Teed, Jia Deng
We address multiview stereo (MVS), an important 3D vision task that reconstructs a 3D model such as a dense point cloud from multiple calibrated images. We propose CER-MVS (Cascade…
Coupled Iterative Refinement for 6D Multi-Object Pose Estimation
Lahav Lipson, Zachary Teed, Ankit Goyal +1
We address the task of 6D multi-object pose: given a set of known 3D objects and an RGB or RGB-D input image, we detect and estimate the 6D pose of each object. We propose a new ap…
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching
Lahav Lipson, Zachary Teed, Jia Deng
We introduce RAFT-Stereo, a new deep architecture for rectified stereo based on the optical flow network RAFT. We introduce multi-level convolutional GRUs, which more efficiently p…
DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
Zachary Teed, Jia Deng
We introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense Bundle Adjustm…
Tangent Space Backpropagation for 3D Transformation Groups
Zachary Teed, Jia Deng
We address the problem of performing backpropagation for computation graphs involving 3D transformation groups SO(3), SE(3), and Sim(3). 3D transformation groups are widely used in…
RAFT-3D: Scene Flow using Rigid-Motion Embeddings
Zachary Teed, Jia Deng
We address the problem of scene flow: given a pair of stereo or RGB-D video frames, estimate pixelwise 3D motion. We introduce RAFT-3D, a new deep architecture for scene flow. RAFT…
RAFT: Recurrent All-Pairs Field Transforms for Optical Flow
Zachary Teed, Jia Deng
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 volu…
DeepV2D: Video to Depth with Differentiable Structure from Motion
Zachary Teed, Jia Deng
We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video. DeepV2D combines the representation ability of neural networks with the geometric prin…