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End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection [article]

Rui Qian, Divyansh Garg, Yan Wang, Yurong You, Serge Belongie, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
2020 arXiv   pre-print
on the KITTI image-based 3D object detection leaderboard at the time of submission.  ...  PL combines state-of-the-art deep neural networks for 3D depth estimation with those for 3D object detection by converting 2D depth map outputs to 3D point cloud inputs.  ...  We are thankful for generous support by Zillow and SAP America Inc.  ... 
arXiv:2004.03080v2 fatcat:67u6xcafufambnciirggtotfdy

End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

Rui Qian, Divyansh Garg, Yan Wang, Yurong You, Serge Belongie, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
on the KITTI image-based 3D object detection leaderboard at the time of submission.  ...  PL combines state-of-the-art deep neural networks for 3D depth estimation with those for 3D object detection by converting 2D depth map outputs to 3D point cloud inputs.  ...  We are thankful for generous support by Zillow and SAP America Inc.  ... 
doi:10.1109/cvpr42600.2020.00592 dblp:conf/cvpr/QianG0YBHCWC20 fatcat:rrodzozyj5bzfkg73im6jzerlm

3D Object Detection for Autonomous Driving: A Review and New Outlooks [article]

Jiageng Mao, Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
2022 arXiv   pre-print
Second, we conduct a comprehensive survey of the progress in 3D object detection from the aspects of models and sensory inputs, including LiDAR-based, camera-based, and multi-modal detection approaches  ...  This paper reviews the advances in 3D object detection for autonomous driving. First, we introduce the background of 3D object detection and discuss the challenges in this task.  ...  Pseudo-LiDAR based methods transform a depth image into a pseudo-LiDAR point cloud, and LiDAR-based detectors can then be employed to detect 3D objects from the point cloud.  ... 
arXiv:2206.09474v1 fatcat:3skws77uqngjtpo6mycpo4dhny

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving [article]

Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
2020 arXiv   pre-print
At the time of submission our algorithm holds the highest entry on the KITTI 3D object detection leaderboard for stereo-image-based approaches.  ...  Taking the inner workings of convolutional neural networks into consideration, we propose to convert image-based depth maps to pseudo-LiDAR representations --- essentially mimicking the LiDAR signal.  ...  We are thankful for generous support by Zillow and SAP America Inc. We thank Gao Huang for helpful discussion.  ... 
arXiv:1812.07179v6 fatcat:kxknnppa7nb63nfopj6n2zc7ge

Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud [article]

Xinshuo Weng, Kris Kitani
2019 arXiv   pre-print
Then we can train a LiDAR-based 3D detection network with our pseudo-LiDAR end-to-end.  ...  In this work, we aim at bridging the performance gap between 3D sensing and 2D sensing for 3D object detection by enhancing LiDAR-based algorithms to work with single image input.  ...  We show that, from only a single RGB image, the 3D bounding box detection for the car category can be very accurate, even for the challenging faraway objects (e.g., in the 6th row 1st column and 8th row  ... 
arXiv:1903.09847v4 fatcat:vwqdxubedzddnkeuos6eqqgsny

Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
At the time of submission our algorithm holds the highest entry on the KITTI 3D object detection leaderboard for stereo-image-based approaches.  ...  Taking the inner workings of convolutional neural networks into consideration, we propose to convert image-based depth maps to pseudo-LiDAR representations -essentially mimicking the LiDAR signal.  ...  We are thankful for generous support by Zillow and SAP America Inc. We thank Gao Huang for helpful discussion.  ... 
doi:10.1109/cvpr.2019.00864 dblp:conf/cvpr/WangCGHCW19 fatcat:am5a3hz7ajb6tp4cm5ygoghqom

ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection

Zhenbo Xu, Wei Zhang, Xiaoqing Ye, Xiao Tan, Wei Yang, Shilei Wen, Errui Ding, Ajin Meng, Liusheng Huang
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we present a novel framework named ZoomNet for stereo imagery-based 3D detection.  ...  3D object detection is an essential task in autonomous driving and robotics. Though great progress has been made, challenges remain in estimating 3D pose for distant and occluded objects.  ...  Except for Pseudo-LiDAR, all methods are based on 2D detection. Pseudo-LiDAR provides multiple 3D object detectors for chosen. We select the one with the highest performance.  ... 
doi:10.1609/aaai.v34i07.6945 fatcat:wngiiy3npvdxpnxvz5vzjassnm

Rethinking Pseudo-LiDAR Representation [article]

Xinzhu Ma, Shinan Liu, Zhiyi Xia, Hongwen Zhang, Xingyu Zeng, Wanli Ouyang
2020 arXiv   pre-print
The recently proposed pseudo-LiDAR based 3D detectors greatly improve the benchmark of monocular/stereo 3D detection task. However, the underlying mechanism remains obscure to the research community.  ...  Based on this observation, we design an image based CNN detector named Patch-Net, which is more generalized and can be instantiated as pseudo-LiDAR based 3D detectors.  ...  MonoDIS [34] disentangles the loss for 2D and 3D detection and jointly trains these two tasks in an end-to-end manner.  ... 
arXiv:2008.04582v1 fatcat:k4spphwz45hjtpifiy3fx763de

ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection [article]

Zhenbo Xu, Wei Zhang, Xiaoqing Ye, Xiao Tan, Wei Yang, Shilei Wen, Errui Ding, Ajin Meng, Liusheng Huang
2020 arXiv   pre-print
In this paper, we present a novel framework named ZoomNet for stereo imagery-based 3D detection.  ...  3D object detection is an essential task in autonomous driving and robotics. Though great progress has been made, challenges remain in estimating 3D pose for distant and occluded objects.  ...  Except for Pseudo-LiDAR, all methods are based on 2D detection. Pseudo-LiDAR provides multiple 3D object detectors for chosen. We select the one with the highest performance.  ... 
arXiv:2003.00529v1 fatcat:kfcz5eoqvnaflhhlfqt6mkeb7a

Is Pseudo-Lidar needed for Monocular 3D Object detection? [article]

Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, Adrien Gaidon
2021 arXiv   pre-print
Recent progress in 3D object detection from single images leverages monocular depth estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors.  ...  In this work, we propose an end-to-end, single stage, monocular 3D object detector, DD3D, that can benefit from depth pre-training like pseudo-lidar methods, but without their limitations.  ...  Our results indicate an almost perfect linear relationship between depth quality as measured by the abs rel metric and 3D detection accuracy for our PL-based detector.  ... 
arXiv:2108.06417v1 fatcat:bh6utkslwvcmvleslbdj5lmxr4

Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation [article]

Jiaming Sun, Linghao Chen, Yiming Xie, Siyu Zhang, Qinhong Jiang, Xiaowei Zhou, Hujun Bao
2020 arXiv   pre-print
In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images.  ...  To address the challenge from scarcity of disparity annotation in training, we propose to use a statistical shape model to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds  ...  for 3D object detection.  ... 
arXiv:2004.03572v1 fatcat:zgxv3nmz65de3gyxnalgahdlw4

Multi-Task Multi-Sensor Fusion for 3D Object Detection

Ming Liang, Bin Yang, Yun Chen, Rui Hu, Raquel Urtasun
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection.  ...  Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion.  ...  as pseudo LiDAR points for dense fusion between image and BEV feature maps.  ... 
doi:10.1109/cvpr.2019.00752 dblp:conf/cvpr/LiangYCHU19 fatcat:j2u7e6v2ebgjhmz6z5etjpw7sy

PLUMENet: Efficient 3D Object Detection from Stereo Images [article]

Yan Wang, Bin Yang, Rui Hu, Ming Liang, Raquel Urtasun
2021 arXiv   pre-print
Existing approaches tackle this problem in two steps: first depth estimation from stereo images is performed to produce a pseudo LiDAR point cloud, which is then used as input to a 3D object detector.  ...  Specifically, we directly construct a pseudo LiDAR feature volume (PLUME) in 3D space, which is then used to solve both depth estimation and object detection tasks.  ...  CONCLUSION We have proposed PLUMENet, a model that utilizes efficient pseudo LiDAR feature volume representation for stereo-based BEV object detection.  ... 
arXiv:2101.06594v3 fatcat:spdwihruxney5omg23w54gszky

Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information

Henan Hu, Ming Zhu, Muyu Li, Kwok-Leung Chan
2022 Sensors  
Recently, the research on monocular 3D target detection based on pseudo-LiDAR data has made some progress.  ...  Finally, we input the pseudo-LiDAR point cloud to the LiDAR-based algorithm to detect the 3D target. We conducted extensive experiments on the challenging KITTI dataset.  ...  Pseudo-LiDAR Generation Pseudo-LiDAR-based 3D detection methods usually take advantage of well-developed LiDAR-based 3D detection methods.  ... 
doi:10.3390/s22072576 pmid:35408191 pmcid:PMC9003335 fatcat:t3oc4fcjfvgsxaxc2pemlebnle

Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection [article]

Qian Ye, Ling Jiang, Yuyang Du
2022 arXiv   pre-print
to 3D space for subsequent 3D detection.  ...  Monocular 3D object detection is a common solution for low-cost autonomous agents to perceive their surrounding environment.  ...  BEVDet [8] learning Pseudo-LiDAR representation from multi-camera images to performs 3D object detection.  ... 
arXiv:2207.07933v1 fatcat:vnxuik5a7vgdrfaa6xef57wmzu
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