What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation

20 June 2022

Papers citing "What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation"

9 / 9 papers shown

Title
Text3DAug -- Prompted Instance Augmentation for LiDAR Perception Laurenz Reichardt Luca Uhr Oliver Wasenmüller 29 4 0 26 Aug 2024
ALPI: Auto-Labeller with Proxy Injection for 3D Object Detection using 2D Labels Only Saad Lahlali Nicolas Granger Hervé Le Borgne Quoc-Cuong Pham 3DPC 19 0 0 24 Jul 2024
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey Qinfeng Zhu Lei Fan Ningxin Weng 3DPC 10 33 0 23 Aug 2023
Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection Jing-Fu Zhan Tiejun Liu Rengang Li Jingwei Zhang Zhaoxiang Zhang Yuntao Chen 3DPC 30 13 0 22 May 2023
MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency Qiao Wu Jiaqi Yang Kun Sun Chu’ai Zhang Yanning Zhang Mathieu Salzmann 3DPC 23 1 0 16 Mar 2023
Fake it, Mix it, Segment it: Bridging the Domain Gap Between Lidar Sensors Frederik Hasecke Pascal Colling A. Kummert 3DPC 22 3 0 19 Dec 2022
VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion Hanqi Zhu Jiajun Deng Yu Zhang J. Ji Qiuyu Mao Houqiang Li Yanyong Zhang 3DPC 35 131 0 29 Nov 2021
Mix3D: Out-of-Context Data Augmentation for 3D Scenes Alexey Nekrasov Jonas Schult Or Litany Bastian Leibe Francis Engelmann 3DPC 142 154 0 05 Oct 2021
AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation Venice Erin Liong Thi Ngoc Tho Nguyen S. Widjaja Dhananjai Sharma Z. J. Chong 3DPC 132 109 0 09 Dec 2020