Fetching the paper…
Reading the bibliography…
In this paper, we present an extension to LaserNet, an efficient and state-of-the-art LiDAR based 3D object detector.
On the segmentation of 3D LIDAR point clouds
Bertrand Douillard, James Underwood, Noah Kuntz, Vsevolod Vlaskine, Alastair Quadros, Peter Morton, and Alon Frenkel · 2011
Earlier work this paper cites.
Indoor segmentation and support inference from RGBD images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
Earlier work this paper cites.
Learning rich features from RGB-D images for object detection and segmentation
Saurabh Gupta, Ross Girshick, Pablo Arbeláez, and Jitendra Malik · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Learning hierarchical semantic segmentations of LIDAR data
David Dohan, Brian Matejek, and Thomas Funkhouser · 2015
Earlier work this paper cites.
Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
FuseNet: Incorporating depth into semantic segmentation via fusion-based CNN architecture
Caner Hazirbas, Lingni Ma, Csaba Domokos, and Daniel Cremers · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Point cloud labeling using 3D convolutional neural network
Jing Huang and Suya You · 2016
Earlier work this paper cites.
Vehicle detection from 3D lidar using fully convolutional network
Bo Li, Tianlei Zhang, and Tian Xia · 2016
Earlier work this paper cites.
SSD: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg · 2016
Earlier work this paper cites.
Learning common and specific features for RGB-D semantic segmentation with deconvolutional networks
Jinghua Wang, Zhenhua Wang, Dacheng Tao, Simon See, and Gang Wang · 2016
Earlier work this paper cites.
Joint 2D-3D-semantic data for indoor scene understanding
Iro Armeni, Sasha Sax, Amir R. Zamir, and Silvio Savarese · 2017
Cited alongside, same era.
Multi-view 3D object detection network for autonomous driving
Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia · 2017
Cited alongside, same era.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Cited alongside, same era.
3D bounding box estimation using deep learning and geometry
Arsalan Mousavian, Dragomir Anguelov, John Flynn, and Jana Kosecka · 2017
Cited alongside, same era.
RDFNet: RGB-D multi-level residual feature fusion for indoor semantic segmentation
Seong-Jin Park, Ki-Sang Hong, and Seungyong Lee · 2017
Cited alongside, same era.
PointNet++: Deep hierarchical feature learning on point sets in a metric space
Deep continuous fusion for multi-sensor 3D object detection
Ming Liang, Bin Yang, Shenlong Wang, and Raquel Urtasun · 2018
Later among the works it cites.
Frustum pointnets for 3D object detection from RGB-D data
Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J. Guibas · 2018
Later among the works it cites.
Deep parametric continuous convolutional neural networks
Shenlong Wang, Simon Suo, Wei-Chiu Ma, Andrei Pokrovsky, and Raquel Urtasun · 2018
Later among the works it cites.
SqueezeSeg: Convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud
Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer · 2018
Later among the works it cites.
Pointfusion: Deep sensor fusion for 3D bounding box estimation
Danfei Xu, Dragomir Anguelov, and Ashesh Jain · 2018
Later among the works it cites.
HDNET: Exploiting HD maps for 3d object detection
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Charles R. Qi, Li Yi, Hao Su, and Leonidas J. Guibas · 2017
Cited alongside, same era.
OctNet: Learning deep 3D representations at high resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 2017
Cited alongside, same era.
SEGCloud: Semantic segmentation of 3D point clouds
Lyne Tchapmi, Christopher Choy, Iro Armeni, JunYoung Gwak, and Silvio Savarese · 2017
Cited alongside, same era.
A general pipeline for 3D detection of vehicles
Xinxin Du, Marcelo H. Ang, Sertac Karaman, and Daniela Rus · 2018
Cited alongside, same era.
Joint 3D proposal generation and object detection from view aggregation
Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, and Steven L. Waslander · 2018
Cited alongside, same era.
Large-scale point cloud semantic segmentation with superpoint graphs
Loic Landrieu and Martin Simonovsky · 2018
Cited alongside, same era.
PointPillars: Fast encoders for object detection from point clouds
Alex H. Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom · 2018
Cited alongside, same era.
Bin Yang, Ming Liang, and Raquel Urtasun · 2018
Later among the works it cites.
PIXOR: Real-time 3D object detection from point clouds
Bin Yang, Wenjie Luo, and Raquel Urtasun · 2018
Later among the works it cites.
Deep layer aggregation
Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell · 2018
Later among the works it cites.
Efficient convolutions for real-time semantic segmentation of 3D point clouds
Chris Zhang, Wenjie Luo, and Raquel Urtasun · 2018
Later among the works it cites.
Voxelnet: End-to-end learning for point cloud based 3D object detection
Yin Zhou and Oncel Tuzel · 2018
Later among the works it cites.
LaserNet: An efficient probabilistic 3D object detector for autonomous driving
Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, and Carl K. Wellington · 2019
Closest in time.