activity
20202026
most citedEAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association

114 citations · 273 across the 16 of their papers we have counts for

collaborators
Showing 2023Show all

6 papers · 1 filter

cs.CV2023

Language-Assisted 3D Scene Understanding

Yanmin Wu, Qiankun Gao, Renrui Zhang +1

The scale and quality of point cloud datasets constrain the advancement of point cloud learning. Recently, with the development of multi-modal learning, the incorporation of domain…

cs.CV2023★ 2 cited

GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization

Yahao Shi, Yanmin Wu, Chenming Wu +7

This paper presents a 3D Gaussian Inverse Rendering (GIR) method, employing 3D Gaussian representations to effectively factorize the scene into material properties, light, and geom…

cs.RO2023★ 2 cited

UniQuadric: A SLAM Backend for Unknown Rigid Object 3D Tracking and Light-Weight Modeling

Linghao Yang, Yanmin Wu, Yu Deng +3

Tracking and modeling unknown rigid objects in the environment play a crucial role in autonomous unmanned systems and virtual-real interactive applications. However, many existing…

cs.RO2023★ 51 cited

An Object SLAM Framework for Association, Mapping, and High-Level Tasks

Yanmin Wu, Yunzhou Zhang, Delong Zhu +4

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representat…

cs.CV2023★ 1 cited

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

You Shen, Yunzhou Zhang, Yanmin Wu +4

The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object…

cs.CV2023★ 9 cited

Implicit Neural Representation for Cooperative Low-light Image Enhancement

Shuzhou Yang, Moxuan Ding, Yanmin Wu +2

The following three factors restrict the application of existing low-light image enhancement methods: unpredictable brightness degradation and noise, inherent gap between metric-fa…