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20212023
most citedRobust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)

49 citations · 157 across the 12 of their papers we have counts for

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11 papers · 1 filter

cs.CV2023★ 1 cited

PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration

Mingzhi Yuan, Kexue Fu, Zhihao Li +2

Point cloud registration is a task to estimate the rigid transformation between two unaligned scans, which plays an important role in many computer vision applications. Previous le…

cs.CV2023★ 13 cited

The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification

Linhao Qu, Xiaoyuan Luo, Kexue Fu +2

This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed based o…

cs.CV2022★ 49 cited

Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)

Kexue Fu, Jiazheng Luo, Xiaoyuan Luo +3

3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, t…

cs.CV2022★ 1 cited

Boosting Point-BERT by Multi-choice Tokens

Kexue Fu, Mingzhi Yuan, Manning Wang

Masked language modeling (MLM) has become one of the most successful self-supervised pre-training task. Inspired by its success, Point-BERT, as a pioneer work in point cloud, propo…

cs.CV2022★ 3 cited

POS-BERT: Point Cloud One-Stage BERT Pre-Training

Kexue Fu, Peng Gao, ShaoLei Liu +3

Recently, the pre-training paradigm combining Transformer and masked language modeling has achieved tremendous success in NLP, images, and point clouds, such as BERT. However, dire…

cs.CV2022★ 13 cited

Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning

Kexue Fu, Peng Gao, Renrui Zhang +3

In this paper, we propose a simple and general framework for self-supervised point cloud representation learning. Human beings understand the 3D world by extracting two levels of i…

cs.CV2021★ 11 cited

Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results

Liang Pan, Tong Wu, Zhongang Cai +26

As real-scanned point clouds are mostly partial due to occlusions and viewpoints, reconstructing complete 3D shapes based on incomplete observations becomes a fundamental problem f…

cs.CV2021★ 16 cited

DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion

Renrui Zhang, Ziyao Zeng, Ziyu Guo +3

Point cloud processing is a challenging task due to its sparsity and irregularity. Prior works introduce delicate designs on either local feature aggregator or global geometric arc…

cs.CV2021★ 11 cited

A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds

Xiaoyuan Luo, Shaolei Liu, Kexue Fu +2

Deep neural networks have achieved promising performance in supervised point cloud applications, but manual annotation is extremely expensive and time-consuming in supervised learn…

cs.CV2021★ 15 cited

Robust Point Cloud Registration Framework Based on Deep Graph Matching

Kexue Fu, Shaolei Liu, Xiaoyuan Luo +1

3D point cloud registration is a fundamental problem in computer vision and robotics. There has been extensive research in this area, but existing methods meet great challenges in…

cs.CV2021★ 24 cited

Stereo Correspondence and Reconstruction of Endoscopic Data Challenge

Max Allan, Jonathan Mcleod, Congcong Wang +21

The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China. The task was to perform…