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Deep Camera Pose Regression Using Pseudo-LiDAR [article]

Ali Raza, Lazar Lolic, Shahmir Akhter, Alfonso Dela Cruz, Michael Liut
2022 arXiv   pre-print
We then propose FusionLoc, a novel architecture that uses pseudo-LiDAR to regress a 6DOF camera pose.  ...  The results from this architecture are compared against various other state-of-the-art deep pose regression implementations using the 7 Scenes dataset.  ...  Thus, our goals in this paper are as follows: 1) to prove the viability of pseudo-LiDAR within this space; and 2) to propose a novel architecture that uses pseudo-LiDAR to regress a 6DOF camera pose.  ... 
arXiv:2203.00080v1 fatcat:w6owofg27ffarosmlbqrsmumna

Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving [article]

Jingxiao Zheng, Xinwei Shi, Alexander Gorban, Junhua Mao, Yang Song, Charles R. Qi, Ting Liu, Visesh Chari, Andre Cornman, Yin Zhou, Congcong Li, Dragomir Anguelov
2021 arXiv   pre-print
3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps, failure modes for LiDAR  ...  , relative location between the camera and LiDAR, and a high bar for estimation accuracy.  ...  Point Network: The training loss for the regression branch is a Huber loss Lreg on the generated pseudo 3D la- bels, weighted by the reliability rk .  ... 
arXiv:2112.12141v1 fatcat:qxf3mbny3vdlffk4gn2awbwspi

Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview [article]

Zhaoxin Fan, Yazhi Zhu, Yulin He, Qi Sun, Hongyan Liu, Jun He
2022 arXiv   pre-print
Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others.  ...  Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route.  ...  Although PL-MONO (Pseudo-LiDAR) has greatly promoted performance,a substantial performance gap exists between using pseudo-LiDAR and using real LiDAR.  ... 
arXiv:2105.14291v2 fatcat:2kxd4owthvf7tbcbnlqlqu4r3m

Self-Supervised Person Detection in 2D Range Data using a Calibrated Camera [article]

Dan Jia and Mats Steinweg and Alexander Hermans and Bastian Leibe
2021 arXiv   pre-print
Faster R-CNN) on a calibrated camera to automatically generate training labels (called pseudo-labels) for 2D LiDAR-based person detectors.  ...  However, only a few annotated datasets are available for training and testing these deep networks, potentially limiting their performance when deployed in new environments or with different LiDAR models  ...  GENERATING PSEUDO-LABELS We use a calibrated camera to generate pseudo-labels for training a 2D LiDAR-based person detector.  ... 
arXiv:2012.08890v2 fatcat:mtkmjcc22jfn7c6a6vdfa55tgy

Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies [article]

Yu Huang, Yue Chen
2020 arXiv   pre-print
Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019.  ...  This is a survey of autonomous driving technologies with deep learning methods.  ...  [225] extend Pseudo-LiDAR with stereo camera and extremely sparse LiDAR sensor, called Pseudo-LiDAR++, in which a graph-based depth correction algorithm (GDC) refines the depth map by leveraging sparser  ... 
arXiv:2006.06091v3 fatcat:nhdgivmtrzcarp463xzqvnxlwq

DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination [article]

Muhamad Risqi U. Saputra, Pedro P.B. de Gusmao, Chris Xiaoxuan Lu, Yasin Almalioglu, Stefano Rosa, Changhao Chen, Johan Wahlström, Wei Wang, Andrew Markham, Niki Trigoni
2020 arXiv   pre-print
Thermal cameras are commonly used for perception and inspection when the environment has low visibility. However, their use in odometry estimation is hampered by the lack of robust visual features.  ...  However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fail.  ...  As we have different (pseudo) ground truth format to compare with (VICON, Lidar Gmapping, or VINS-Mono), we align the predicted poses with the (pseudo) ground truth using Horn approaches and evaluate only  ... 
arXiv:1909.07231v2 fatcat:w4jtfllubvfsbl6a6lq3qnjxe4

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

Henan Hu, Ming Zhu, Muyu Li, Kwok-Leung Chan
2022 Sensors  
In contrast to LiDAR-based algorithms, the robustness of pseudo-LiDAR methods is still inferior.  ...  Recently, the research on monocular 3D target detection based on pseudo-LiDAR data has made some progress.  ...  Only the camera calibration matrix was used for projecting the depth map to pseudo-LiDAR. Our framework focuses on the BEV and 3D object detection.  ... 
doi:10.3390/s22072576 pmid:35408191 pmcid:PMC9003335 fatcat:t3oc4fcjfvgsxaxc2pemlebnle

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  
Extensive experiments on the popular KITTI 3D detection dataset indicate ZoomNet surpasses all previous state-of-the-art methods by large margins (improved by 9.4% on APbv (IoU=0.7) over pseudo-LiDAR).  ...  Though great progress has been made, challenges remain in estimating 3D pose for distant and occluded objects.  ...  The concatenated left-right RoI features are used to regress 2D key-points and 3D dimensions.  ... 
doi:10.1609/aaai.v34i07.6945 fatcat:wngiiy3npvdxpnxvz5vzjassnm

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
Extensive experiments on the popular KITTI 3D detection dataset indicate ZoomNet surpasses all previous state-of-the-art methods by large margins (improved by 9.4% on APbv (IoU=0.7) over pseudo-LiDAR).  ...  Though great progress has been made, challenges remain in estimating 3D pose for distant and occluded objects.  ...  The concatenated left-right RoI features are used to regress 2D key-points and 3D dimensions.  ... 
arXiv:2003.00529v1 fatcat:kfcz5eoqvnaflhhlfqt6mkeb7a

A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection

Seong-heum Kim, Youngbae Hwang
2021 Electronics  
Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints.  ...  For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes.  ...  Conversion of an estimated depth map Pseudo-LiDAR [23] Represent.  ... 
doi:10.3390/electronics10040517 fatcat:rziqhrkefvelpg3vgb6qxfprte

Object-Centric Stereo Matching for 3D Object Detection [article]

Alex D. Pon, Jason Ku, Chengyao Li, Steven L. Waslander
2020 arXiv   pre-print
Using stereo cameras to solve this task is a cost-effective alternative to the widely used LiDAR sensor.  ...  Safe autonomous driving requires reliable 3D object detection-determining the 6 DoF pose and dimensions of objects of interest.  ...  Existing methods can be categorized by the sensors they use: LiDAR [1] , [2] , [3] , [4] , [5] , LiDAR and camera [6] , [7] , [8] , monocular camera [9] , [10] , [11] , and stereo camera setups  ... 
arXiv:1909.07566v2 fatcat:pqlye2plqfbvvjoocnetvqy5d4

Hallucinating Dense Optical Flow from Sparse Lidar for Autonomous Vehicles [article]

Victor Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer
2018 arXiv   pre-print
To train this network we introduce a dataset with approximately 20K lidar samples of the Kitti dataset which we have augmented with a pseudo ground-truth image-based optical flow computed using FlowNet2  ...  We demonstrate the effectiveness of our approach on Kitti, and show that despite using the low-resolution and sparse measurements of the lidar, we can regress dense optical flow maps which are at par with  ...  For that we use the MatConvNet [33] Deep Learning Framework.  ... 
arXiv:1808.10542v1 fatcat:3jwwwzmgobf4jlm5ceflqrcu7q

Monocular 3D Object Detection: An Extrinsic Parameter Free Approach [article]

Yunsong Zhou, Yuan He, Hongzi Zhu, Cheng Wang, Hongyang Li, Qinhong Jiang
2021 arXiv   pre-print
To this end, we propose a novel method to capture camera pose to formulate the detector free from extrinsic perturbation.  ...  Due to the lack of insight in industrial application, existing methods on open datasets neglect the camera pose information, which inevitably results in the detector being susceptible to camera extrinsic  ...  Camera pose detection. For camera extrinsic parame- ters regression study, we evaluate the angular errors of the 5.  ... 
arXiv:2106.15796v2 fatcat:gobjw2dp2ffifgzndlsfmkmiay

PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization [article]

Wei Wang, Bing Wang, Peijun Zhao, Changhao Chen, Ronald Clark, Bo Yang, Andrew Markham, Niki Trigoni
2021 arXiv   pre-print
However, LiDAR point clouds are unordered and unstructured making it difficult to apply traditional deep learning regression models for this task.  ...  In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input, without requiring  ...  PROBLEM STATEMENT We design a DNN-based framework for performing deep global pose regression using point cloud data from a LiDAR sensor, which is LiDAR relocalization.  ... 
arXiv:2003.02392v3 fatcat:otfe45l3rza5nkashcwyf72ree

3D Pedestrian Detection in Farmland by Monocular RGB Image and Far-Infrared Sensing

Wei Tian, Zhenwen Deng, Dong Yin, Zehan Zheng, Yuyao Huang, Xin Bi
2021 Remote Sensing  
The Pseudo-LiDAR [27] employed the DORN [12] network to estimate the pixel depth, then converted the depth map to the pseudo point cloud using the corresponding geometric relationship.  ...  [14] designed networks to respectively predict the depth of each pixel and the camera pose.  ... 
doi:10.3390/rs13152896 fatcat:bzhakednbrcvjhgnna7lnowteq
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