no code implementations • 2 Nov 2023 • Wentao Yuan, Adithyavairavan Murali, Arsalan Mousavian, Dieter Fox
With the advent of large language models and large-scale robotic datasets, there has been tremendous progress in high-level decision-making for object manipulation.
no code implementations • 15 Oct 2023 • Chahyon Ku, Carl Winge, Ryan Diaz, Wentao Yuan, Karthik Desingh
We present a novel task scenario designed to evaluate the progress in visuomotor policy learning, with a specific focus on improving the robustness of intricate assembly tasks that require both geometrical and spatial reasoning.
1 code implementation • 21 Jul 2022 • Yikang Ding, Qingtian Zhu, Xiangyue Liu, Wentao Yuan, Haotian Zhang, Chi Zhang
Supervised multi-view stereo (MVS) methods have achieved remarkable progress in terms of reconstruction quality, but suffer from the challenge of collecting large-scale ground-truth depth.
1 code implementation • 21 Jul 2022 • Wentao Yuan, Qingtian Zhu, Xiangyue Liu, Yikang Ding, Haotian Zhang, Chi Zhang
Recently, Implicit Neural Representations (INRs) parameterized by neural networks have emerged as a powerful and promising tool to represent different kinds of signals due to its continuous, differentiable properties, showing superiorities to classical discretized representations.
1 code implementation • CVPR 2022 • Yikang Ding, Wentao Yuan, Qingtian Zhu, Haotian Zhang, Xiangyue Liu, Yuanjiang Wang, Xiao Liu
We analogize MVS back to its nature of a feature matching task and therefore propose a powerful Feature Matching Transformer (FMT) to leverage intra- (self-) and inter- (cross-) attention to aggregate long-range context information within and across images.
Ranked #8 on 3D Reconstruction on DTU
1 code implementation • 8 Sep 2021 • Wentao Yuan, Chris Paxton, Karthik Desingh, Dieter Fox
Sequential manipulation tasks require a robot to perceive the state of an environment and plan a sequence of actions leading to a desired goal state.
no code implementations • CVPR 2021 • Benjamin Eckart, Wentao Yuan, Chao Liu, Jan Kautz
In this work, we introduce a general method for 3D self-supervised representation learning that 1) remains agnostic to the underlying neural network architecture, and 2) specifically leverages the geometric nature of 3D point cloud data.
no code implementations • CVPR 2021 • Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, Steven Lovegrove
We achieve this by jointly optimizing the parameters of two neural radiance fields and a set of rigid poses which align the two fields at each frame.
2 code implementations • ECCV 2020 • Wentao Yuan, Ben Eckart, Kihwan Kim, Varun Jampani, Dieter Fox, Jan Kautz
Point cloud registration is a fundamental problem in 3D computer vision, graphics and robotics.
1 code implementation • 27 Nov 2018 • Wentao Yuan, David Held, Christoph Mertz, Martial Hebert
Recently, neural networks operating on point clouds have shown superior performance on 3D understanding tasks such as shape classification and part segmentation.
5 code implementations • 2 Aug 2018 • Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, Martial Hebert
Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications.
Ranked #6 on Point Cloud Completion on Completion3D