10 citations · 38 across the 7 of their papers we have counts for
8 papers
S4VY: Segment Anything in Feed-Forward 4D Visual Geometry
Jingdong Zhang, Xin Li, Jan Kautz +2
Accurate instance segmentation in dynamic scenes is important for downstream applications such as robotics and autonomous driving. Existing Segment Anything models operate primaril…
SpaCeFormer: Fast Proposal-Free Open-Vocabulary 3D Instance Segmentation
Chris Choy, Junha Lee, Chunghyun Park +2
Open-vocabulary 3D instance segmentation is a core capability for robotics and AR/VR, but prior methods trade one bottleneck for another: multi-stage 2D+3D pipelines aggregate foun…
FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
Bowen Wen, Wei Yang, Jan Kautz +1
We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups. Our approach can be instantly a…
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
Bowen Wen, Jonathan Tremblay, Valts Blukis +6
We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the obj…
RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis
Jonathan Tremblay, Moustafa Meshry, Alex Evans +9
We present a large-scale synthetic dataset for novel view synthesis consisting of ~300k images rendered from nearly 2000 complex scenes using high-quality ray tracing at high resol…
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
Yu-Wei Chao, Wei Yang, Yu Xiang +9
We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first compare DexYCB with a related one through cross-dataset evaluation. We then present a thorough b…
Displacement-Invariant Cost Computation for Efficient Stereo Matching
Yiran Zhong, Charles Loop, Wonmin Byeon +7
Although deep learning-based methods have dominated stereo matching leaderboards by yielding unprecedented disparity accuracy, their inference time is typically slow, on the order…
Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations
Jonathan Tremblay, Thang To, Artem Molchanov +3
We present a system to infer and execute a human-readable program from a real-world demonstration. The system consists of a series of neural networks to perform perception, program…