Search Results for author: Wentao Yuan

Found 7 papers, 5 papers with code

TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers

1 code implementation29 Nov 2021 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.

3D Reconstruction

SORNet: Spatial Object-Centric Representations for Sequential Manipulation

1 code implementation8 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, where the ability to reason about spatial relationships among object entities from raw sensor inputs is crucial.

Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models

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.

Point Cloud Segmentation Representation Learning +3

STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering

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.

Frame Neural Rendering

Iterative Transformer Network for 3D Point Cloud

1 code implementation27 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.

Frame General Classification

PCN: Point Completion Network

5 code implementations2 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.

Point Cloud Completion

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