activity
20142024
most citedCorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence

95 citations · 142 across the 10 of their papers we have counts for

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Showing cs.CVShow all

9 papers · 1 filter

cs.CV2024

Oracle Bone Script Similiar Character Screening Approach Based on Simsiam Contrastive Learning and Supervised Learning

Xinying Weng, Yifan Li, Shuaidong Hao +1

This project proposes a new method that uses fuzzy comprehensive evaluation method to integrate ResNet-50 self-supervised and RepVGG supervised learning. The source image dataset H…

cs.CV2024

OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking

Zekun Qian, Ruize Han, Wei Feng +3

We study a novel yet practical problem of open-corpus multi-object tracking (OCMOT), which extends the MOT into localizing, associating, and recognizing generic-category objects of…

cs.CV2024

RainyScape: Unsupervised Rainy Scene Reconstruction using Decoupled Neural Rendering

Xianqiang Lyu, Hui Liu, Junhui Hou

We propose RainyScape, an unsupervised framework for reconstructing clean scenes from a collection of multi-view rainy images. RainyScape consists of two main modules: a neural ren…

cs.CV2024

ParaPoint: Learning Global Free-Boundary Surface Parameterization of 3D Point Clouds

Qijian Zhang, Junhui Hou, Ying He

Surface parameterization is a fundamental geometry processing problem with rich downstream applications. Traditional approaches are designed to operate on well-behaved mesh models…

cs.CV20241 cited

Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection

Shiwen Zhao, Wei Wang, Junhui Hou +1

This paper introduces HPC-Net, a high-precision and rapidly convergent object detection network.

cs.CV202343 cited

Global Structure-Aware Diffusion Process for Low-Light Image Enhancement

Jinhui Hou, Zhiyu Zhu, Junhui Hou +3

This paper studies a diffusion-based framework to address the low-light image enhancement problem. To harness the capabilities of diffusion models, we delve into this intricate pro…