Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across
Objects and Views

Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across Objects and Views

16 September 2021

Robert McCraith

Eldar Insafutdinov

Andrea Vedaldi

Papers citing "Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across Objects and Views"

6 / 6 papers shown

Title
MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator Chang Liu Xiaoyan Qian Xiaojuan Qi E. Lam Siew-Chong Tan Ngai Wong 3DPC 31 11 0 29 Mar 2022
Weakly Supervised 3D Object Detection from Point Clouds Zengyi Qin Jinglu Wang Yan Lu 3DPC 77 62 0 28 Jul 2020
Shape and Viewpoint without Keypoints Shubham Goel Angjoo Kanazawa Jitendra Malik 3DV 97 108 0 21 Jul 2020
Deep Ordinal Regression Network for Monocular Depth Estimation Huan Fu Biwei Huang Chaohui Wang Kayhan Batmanghelich Dacheng Tao MDE 200 1,708 0 06 Jun 2018
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 14,131 0 02 Dec 2016
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks Martin Engelcke Dushyant Rao Dominic Zeng Wang Chi Hay Tong Ingmar Posner 3DPC 192 522 0 21 Sep 2016