See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation

17 November 2021

Papers citing "See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation"

8 / 8 papers shown

Title
Robust Unsupervised Domain Adaptation for 3D Point Cloud Segmentation Under Source Adversarial Attacks Yiming Li Junjie Chen Yuecong Xu Kemi Ding 3DPC 42 0 0 02 Apr 2025
Syn-to-Real Unsupervised Domain Adaptation for Indoor 3D Object Detection Yunsong Wang Na Zhao Gim Hee Lee 3DPC 40 1 0 17 Jun 2024
LiDAR Meta Depth Completion Wolfgang Boettcher Lukas Hoyer Ozan Unal Ke Li Dengxin Dai 25 1 0 24 Jul 2023
MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection Darren Tsai J. S. Berrio Mao Shan E. Nebot Stewart Worrall 3DPC 40 10 0 05 Apr 2023
Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level Sebastian Huch Luca Scalerandi Esteban Rivera Markus Lienkamp 3DPC 21 16 0 03 Mar 2023
Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection Jiajun Deng Shaoshuai Shi Pei-Cian Li Wen-gang Zhou Yanyong Zhang Houqiang Li 3DPC 231 660 0 31 Dec 2020
Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions Inigo Alonso L. Riazuelo Luis Montesano Ana C. Murillo 3DPC OOD 56 29 0 23 Oct 2020
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas J. Guibas 3DH 3DPC 3DV PINN 222 14,103 0 02 Dec 2016