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20202022
most citedFine-Grained Object Classification via Self-Supervised Pose Alignment

7 citations · 14 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.CV2022

BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation

Zelin Xu, Yichen Zhang, Ke Chen +1

The challenges of learning a robust 6D pose function lie in 1) severe occlusion and 2) systematic noises in depth images. Inspired by the success of point-pair features, the goal o…

cs.CV20227 cited

Fine-Grained Object Classification via Self-Supervised Pose Alignment

Xuhui Yang, Yaowei Wang, Ke Chen +2

Semantic patterns of fine-grained objects are determined by subtle appearance difference of local parts, which thus inspires a number of part-based methods. However, due to uncontr…

cs.CV20214 cited

3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding

Shengheng Deng, Xun Xu, Chaozheng Wu +2

The ability to understand the ways to interact with objects from visual cues, a.k.a. visual affordance, is essential to vision-guided robotic research. This involves categorizing,…

cs.CV20203 cited

CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling

Jiehong Lin, Xian Shi, Yuan Gao +2

Point set is arguably the most direct approximation of an object or scene surface, yet its practical acquisition often suffers from the shortcoming of being noisy, sparse, and poss…

cs.CV2020

Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors

Lulu Tang, Ke Chen, Chaozheng Wu +3

Existing deep learning algorithms for point cloud analysis mainly concern discovering semantic patterns from global configuration of local geometries in a supervised learning manne…