What You See is What You Get: Exploiting Visibility for 3D Object Detection

10 December 2019

Papers citing "What You See is What You Get: Exploiting Visibility for 3D Object Detection"

9 / 9 papers shown

Title
HDNET: Exploiting HD Maps for 3D Object Detection Binh Yang Ming Liang R. Urtasun 3DPC 3DV 49 320 0 21 Dec 2020
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks Chris Choy JunYoung Gwak Silvio Savarese 3DPC 104 1,768 0 18 Apr 2019
nuScenes: A multimodal dataset for autonomous driving Holger Caesar Varun Bankiti Alex H. Lang Sourabh Vora Venice Erin Liong Qiang Xu Anush Krishnan Yuxin Pan G. Baldan Oscar Beijbom 3DPC 210 5,653 0 26 Mar 2019
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving Gregory P. Meyer A. Laddha E. Kee Carlos Vallespi-Gonzalez Carl K. Wellington 3DPC 43 338 0 20 Mar 2019
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud Shaoshuai Shi Xiaogang Wang Hongsheng Li 3DPC 131 2,392 0 11 Dec 2018
PointConv: Deep Convolutional Networks on 3D Point Clouds Wenxuan Wu Zhongang Qi Fuxin Li 3DPC 98 1,552 0 17 Nov 2018
Frustum PointNets for 3D Object Detection from RGB-D Data C. Qi Wen Liu Chenxia Wu Hao Su Leonidas Guibas 3DPC 114 2,257 0 22 Nov 2017
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 400 3,264 0 24 Nov 2016
Cyclical Learning Rates for Training Neural Networks L. Smith ODL 88 2,515 0 03 Jun 2015