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Laser scanner data. Use both for obstacles and collecting general data.
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Monte Carlo methods
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Lehtomaki, M.,
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1601
Accuracy
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Kellner, D.,
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Klappstein, J.,
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Tracking of Extended Objects with High-Resolution Doppler Radar,
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1605
Doppler radar
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Luo, H.[Huan],
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Li, J.[Jonathan],
Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning
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1807
Markov processes, image representation, image segmentation,
learning (artificial intelligence), optical radar,
semantic labeling
See also Capsule-Based Networks for Road Marking Extraction and Classification from Mobile LiDAR Point Clouds.
BibRef
Nijsure, Y.A.,
Kaddoum, G.,
Khaddaj Mallat, N.,
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Cognitive Chaotic UWB-MIMO Detect-Avoid Radar for Autonomous UAV
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ITS(17), No. 11, November 2016, pp. 3121-3131.
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1609
Bayes methods
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Jung, H.[Ha_Rim],
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The SSP-Tree: A Method for Distributed Processing of Range Monitoring
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IJGI(6), No. 11, 2017, pp. xx-yy.
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1712
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Deng, Z.,
Zhou, L.,
Detection and Recognition of Traffic Planar Objects Using Colorized
Laser Scan and Perspective Distortion Rectification,
ITS(19), No. 5, May 2018, pp. 1485-1495.
IEEE DOI
1805
Cameras, Distortion, Feature extraction, Image color analysis,
Laser noise, Shape, Autonomous vehicle,
planar object detection and recognition
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Hossain, M.A.[Md Anowar],
Elshafiey, I.[Ibrahim],
Al-Sanie, A.[Abdulhameed],
Waveform diversity for mutual interference mitigation in automotive
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SIViP(13), No. 1, February 2019, pp. 1-8.
Springer DOI
1901
BibRef
Stateczny, A.[Andrzej],
Kazimierski, W.[Witold],
Gronska-Sledz, D.[Daria],
Motyl, W.[Weronika],
The Empirical Application of Automotive 3D Radar Sensor for Target
Detection for an Autonomous Surface Vehicle's Navigation,
RS(11), No. 10, 2019, pp. xx-yy.
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1906
BibRef
Lee, S.,
Lee, B.,
Lee, J.,
Kim, S.,
Statistical Characteristic-Based Road Structure Recognition in
Automotive FMCW Radar Systems,
ITS(20), No. 7, July 2019, pp. 2418-2429.
IEEE DOI
1907
Roads, Automotive engineering, Radar detection, Vehicles,
Support vector machines, Radar measurements,
support vector machine (SVM)
BibRef
Parmar, Y.[Yashrajsinh],
Natarajan, S.[Sudha],
Sobha, G.[Gayathri],
DeepRange: deep-learning-based object detection and ranging in
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IET-ITS(13), No. 8, August 2019, pp. 1256-1264.
DOI Link
1908
BibRef
Chen, S.,
Niu, S.,
Lan, T.,
Liu, B.,
PCT: Large-Scale 3d Point Cloud Representations Via Graph Inception
Networks with Applications to Autonomous Driving,
ICIP19(4395-4399)
IEEE DOI
1910
3D point cloud representations, graph deep neural networks, autonomous driving
BibRef
Lee, J.,
Jo, J.,
Park, T.,
Segmentation of Vehicles and Roads by a Low-Channel Lidar,
ITS(20), No. 11, November 2019, pp. 4251-4256.
IEEE DOI
1911
Laser radar, Convolution,
Image segmentation, Roads, Autonomous vehicles, receptive field
BibRef
Jung, D.H.[Dae-Hwan],
Kang, H.S.[Hyun-Seong],
Kim, C.K.[Chul-Ki],
Park, J.[Junhyeong],
Park, S.O.[Seong-Ook],
Sparse Scene Recovery for High-Resolution Automobile FMCW SAR via
Scaled Compressed Sensing,
GeoRS(57), No. 12, December 2019, pp. 10136-10146.
IEEE DOI
1912
Automobile frequency-modulated continuous-wave synthetic aperture radar.
Synthetic aperture radar, Automobiles, Azimuth,
Image reconstruction, Bandwidth, Radar polarimetry, sparse reconstruction
BibRef
Mei, J.L.[Ji-Lin],
Gao, B.[Biao],
Xu, D.H.[Dong-Hao],
Yao, W.[Wen],
Zhao, X.[Xijun],
Zhao, H.J.[Hui-Jing],
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using
Semi-Supervised Learning,
ITS(21), No. 6, June 2020, pp. 2496-2509.
IEEE DOI
2006
Image segmentation, Semantics,
Laser radar, Semisupervised learning, Feature extraction,
semi-supervised learning
BibRef
Fazekas, A.,
Oeser, M.,
Performance Metrics and Validation Methods for Vehicle Position
Estimators,
ITS(21), No. 7, July 2020, pp. 2853-2863.
IEEE DOI
2007
Measurement, Radar tracking, Solid modeling, Microscopy, Sensors,
Estimation, Data acquisition, Vehicle tracking,
vision based traffic analysis
BibRef
Hong, D.S.[Dza-Shiang],
Chen, H.H.[Hung-Hao],
Hsiao, P.Y.[Pei-Yung],
Fu, L.C.[Li-Chen],
Siao, S.M.[Siang-Min],
CrossFusion net: Deep 3D object detection based on RGB images and
point clouds in autonomous driving,
IVC(100), 2020, pp. 103955.
Elsevier DOI
2008
Deep learning, 3D object detection, Data fusion, Autonomous driving
BibRef
Geng, K.[Keke],
Dong, G.[Ge],
Yin, G.D.[Guo-Dong],
Hu, J.Y.[Jing-Yu],
Deep Dual-Modal Traffic Objects Instance Segmentation Method Using
Camera and LIDAR Data for Autonomous Driving,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link
2010
BibRef
Lee, S.,
Lee, J.Y.,
Kim, S.C.,
Mutual Interference Suppression Using Wavelet Denoising in Automotive
FMCW Radar Systems,
ITS(22), No. 2, February 2021, pp. 887-897.
IEEE DOI
2102
Radar, Automotive engineering, Noise reduction, Wavelet transforms,
Sensors, Interference suppression,
wavelet denoising
BibRef
Guo, W.Z.[Wen-Zhong],
Chen, J.W.[Jia-Wei],
Wang, W.P.[Wei-Peng],
Luo, H.[Huan],
Wang, S.P.[Shi-Ping],
Three-Dimensional Object Co-Localization from Mobile LiDAR Point
Clouds,
ITS(22), No. 4, April 2021, pp. 1996-2007.
IEEE DOI
2104
Object detection, Laser radar, graph matching,
Task analysis, Semantics, Search problems, Location awareness.
BibRef
Zhang, L.W.[Li-Wen],
Zheng, J.Y.[Jian-Ying],
Sun, R.C.[Rong-Chuan],
Tao, Y.Y.[Yan-Yun],
GC-Net: Gridding and Clustering for Traffic Object Detection With
Roadside LiDAR,
IEEE_Int_Sys(36), No. 4, July 2021, pp. 104-113.
IEEE DOI
2109
Laser radar, Feature extraction,
Intelligent systems, Data structures, Surveillance, Roads, detection,
intelligent transportation system
BibRef
Li, J.[Jiong],
Zhang, Y.[Yu],
Liu, X.X.[Xi-Xia],
Zhang, X.D.[Xu-Dong],
Bai, R.[Rui],
Obstacle detection and tracking algorithm based on multi-lidar fusion
in urban environment,
IET-ITS(15), No. 11, 2021, pp. 1372-1387.
DOI Link
2110
Autonomous Vehicle, Lidar, Obstacle detection and tracking, Sensor fusion
BibRef
Guo, R.[Rui],
Li, D.[Deng],
Han, Y.H.[Ya-Hong],
Deep multi-scale and multi-modal fusion for 3D object detection,
PRL(151), 2021, pp. 236-242.
Elsevier DOI
2110
3D Object detection, Feature fusion, Autonomous driving, Point cloud
BibRef
Sun, X.B.[Xue-Bin],
Wang, S.[Sukai],
Liu, M.[Ming],
A Novel Coding Architecture for Multi-Line LiDAR Point Clouds Based
on Clustering and Convolutional LSTM Network,
ITS(23), No. 3, March 2022, pp. 2190-2201.
IEEE DOI
2203
Image coding, Laser radar, Redundancy, Sensors,
Prediction algorithms, Heuristic algorithms, LiDAR,
convolutional LSTM
BibRef
Yuan, Z.X.[Zhen-Xun],
Song, X.[Xiao],
Bai, L.[Lei],
Wang, Z.[Zhe],
Ouyang, W.L.[Wan-Li],
Temporal-Channel Transformer for 3D Lidar-Based Video Object
Detection for Autonomous Driving,
CirSysVideo(32), No. 4, April 2022, pp. 2068-2078.
IEEE DOI
2204
Object detection, Feature extraction, Laser radar, Correlation,
Decoding, Head, Lidar-based video, 3D object detection, transformer,
temporal-channel attention
BibRef
Mothershed, D.M.[David Michael],
Lugner, R.[Robert],
Afraj, S.[Shahabaz],
Sequeira, G.J.[Gerald Joy],
Schneider, K.[Kilian],
Brandmeier, T.[Thomas],
Soloiu, V.[Valentin],
Comparison and Evaluation of Algorithms for LiDAR-Based Contour
Estimation in Integrated Vehicle Safety,
ITS(23), No. 5, May 2022, pp. 3925-3942.
IEEE DOI
2205
Safety, Estimation, Laser radar, Accidents, Shape, Cameras, Sensors,
Contour estimation, curve similarity, integrated safety,
light detection and ranging (LiDAR)
BibRef
Zhang, J.X.[Jia-Xing],
Xiao, W.[Wen],
Mills, J.P.[Jon P.],
Optimizing Moving Object Trajectories from Roadside Lidar Data by
Joint Detection and Tracking,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Li, Y.J.[Yu-Jie],
Yang, S.[Shuo],
Zheng, Y.C.[Yu-Chao],
Lu, H.M.[Hui-Min],
Improved Point-Voxel Region Convolutional Neural Network:
3D Object Detectors for Autonomous Driving,
ITS(23), No. 7, July 2022, pp. 9311-9317.
IEEE DOI
2207
Feature extraction, Proposals, Training, Detectors, Object detection,
Convolution, 3D object detection, region proposal method,
point cloud data processing
BibRef
Solomitckii, D.[Dmitrii],
Heino, M.[Mikko],
Buddappagari, S.[Sreehari],
Hein, M.A.[Matthias A.],
Valkama, M.[Mikko],
Radar Scheme With Raised Reflector for NLOS Vehicle Detection,
ITS(23), No. 7, July 2022, pp. 9037-9045.
IEEE DOI
2207
Solid modeling, Radar, Automobiles, Radar cross-sections,
Backscatter, Radar detection, Radar cross section, non-line-of-sight
BibRef
Zou, X.F.[Xiao-Feng],
Li, K.[Kenli],
Li, Y.F.[Yang-Fan],
Wei, W.[Wei],
Chen, C.[Cen],
Multi-Task Y-Shaped Graph Neural Network for Point Cloud Learning in
Autonomous Driving,
ITS(23), No. 7, July 2022, pp. 9568-9579.
IEEE DOI
2207
Point cloud compression, Task analysis, Feature extraction,
Multitasking, Graph neural networks, Semantics, Y-shaped architecture
BibRef
Zhu, B.[Bing],
Sun, Y.H.[Yu-Hang],
Zhao, J.[Jian],
Zhang, S.[Sumin],
Zhang, P.X.[Pei-Xing],
Song, D.J.[Dong-Jian],
Millimeter-Wave Radar in-the-Loop Testing for Intelligent Vehicles,
ITS(23), No. 8, August 2022, pp. 11126-11136.
IEEE DOI
2208
Testing, Radar, Intelligent vehicles, Millimeter wave radar,
Radar cross-sections, Mathematical model, Meteorology,
radar in-the-loop test
BibRef
Iqbal, H.[Hafsa],
Campo, D.[Damian],
Marin-Plaza, P.[Pablo],
Marcenaro, L.[Lucio],
Gómez, D.M.[David Martín],
Regazzoni, C.[Carlo],
Modeling Perception in Autonomous Vehicles via 3D Convolutional
Representations on LiDAR,
ITS(23), No. 9, September 2022, pp. 14608-14619.
IEEE DOI
2209
Laser radar, Feature extraction, Point cloud compression,
Autonomous vehicles, Cameras, Solid modeling,
hierarchical generalize dynamic Bayesian network
BibRef
Fu, H.[Hao],
Xue, H.Z.[Han-Zhang],
Xie, G.L.[Guang-Lei],
MapCleaner: Efficiently Removing Moving Objects from Point Cloud Maps
in Autonomous Driving Scenarios,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Duy, L.H.[Loc Hoang],
Kim, G.W.[Gon-Woo],
AEC3D: An Efficient and Compact Single Stage 3D Multiobject Detector
for Autonomous Driving,
ITS(23), No. 12, December 2022, pp. 23422-23432.
IEEE DOI
2212
Point cloud compression, Feature extraction, Detectors,
Object detection, Laser radar, Real-time systems,
convolutional neural network
BibRef
Muńoz-Bańón, M.Á.[Miguel Ángel],
Velasco-Sánchez, E.[Edison],
Candelas, F.A.[Francisco A.],
Torres, F.[Fernando],
OpenStreetMap-Based Autonomous Navigation With LiDAR
Naive-Valley-Path Obstacle Avoidance,
ITS(23), No. 12, December 2022, pp. 24428-24438.
IEEE DOI
2212
Roads, Location awareness, Autonomous robots, Laser radar, Costs,
Navigation, Collision avoidance, Autonomous navigation,
LiDAR point cloud
BibRef
Abbasi, R.[Rashid],
Bashir, A.K.[Ali Kashif],
Alyamani, H.J.[Hasan J.],
Amin, F.[Farhan],
Doh, J.[Jaehyeok],
Chen, J.W.[Jian-Wen],
Lidar Point Cloud Compression, Processing and Learning for Autonomous
Driving,
ITS(24), No. 1, January 2023, pp. 962-979.
IEEE DOI
2301
Image coding, Laser radar, Real-time systems, Safety,
Point cloud compression, Vehicular ad hoc networks, deep learning
BibRef
He, Q.[Qingdong],
Wang, Z.[Zhengning],
Zeng, H.[Hao],
Zeng, Y.[Yi],
Liu, Y.J.[Yi-Jun],
Liu, S.C.[Shuai-Cheng],
Zeng, B.[Bing],
Stereo RGB and Deeper LIDAR-Based Network for 3D Object Detection in
Autonomous Driving,
ITS(24), No. 1, January 2023, pp. 152-162.
IEEE DOI
2301
Point cloud compression, Feature extraction, Proposals, Object detection,
Laser radar, Semantics, 3D object detection, deeper LIDAR features
BibRef
Ci, W.[Wenyan],
Xu, T.[Tie],
Lin, R.[Runze],
Lu, S.[Shan],
Wu, X.[Xialai],
Xuan, J.Y.[Jia-Yin],
A Novel Method for Obstacle Detection in Front of Vehicles Based on
the Local Spatial Features of Point Cloud,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Bauer, P.[Péter],
Hiba, A.[Antal],
Nagy, M.[Mihály],
Simonyi, E.[Erno],
Kuna, G.I.[Gergely István],
Kisari, Á.[Ádám],
Drotár, I.[István],
Zarándy, Á.[Ákos],
Encounter Risk Evaluation with a Forerunner UAV,
RS(15), No. 6, 2023, pp. 1512.
DOI Link
2304
Downward-looking camera flying in front of the
emergency ground vehicles to look for danger.
BibRef
Gao, A.[Aqi],
Cao, J.[Jiale],
Pang, Y.W.[Yan-Wei],
Li, X.L.[Xue-Long],
Real-Time Stereo 3D Car Detection With Shape-Aware Non-Uniform
Sampling,
ITS(24), No. 4, April 2023, pp. 4027-4037.
IEEE DOI
2304
Feature extraction, Automobiles,
Proposals, Object detection, Point cloud compression, Detectors
BibRef
Chandrasegar, V.[Vasantha],
Koh, J.W.[Jinh-Wan],
Estimation of Azimuth Angle Using an Ultrasonic Sensor for Automobile,
RS(15), No. 7, 2023, pp. 1837.
DOI Link
2304
BibRef
Zhuang, G.H.[Geng-Hang],
Bing, Z.S.[Zhen-Shan],
Yao, X.T.[Xiang-Tong],
Huang, Y.H.[Yu-Hong],
Huang, K.[Kai],
Knoll, A.[Alois],
Toward Intelligent Sensing:
Optimizing Lidar Beam Distribution for Autonomous Driving,
ITS(24), No. 8, August 2023, pp. 8386-8392.
IEEE DOI
2308
Laser radar, Autonomous vehicles, Optimization, Sensors, Task analysis,
Object detection, LiDAR sensor, LiDAR optimization, autonomous driving
BibRef
Kim, T.L.[Taek-Lim],
Arshad, S.[Saba],
Park, T.H.[Tae-Hyoung],
Adaptive Feature Attention Module for Robust Visual-LiDAR
Fusion-Based Object Detection in Adverse Weather Conditions,
RS(15), No. 16, 2023, pp. 3992.
DOI Link
2309
BibRef
Han, C.Y.[Chong-Yang],
Wu, W.B.[Wei-Bin],
Luo, X.[Xiwen],
Li, J.[Jiehao],
Visual Navigation and Obstacle Avoidance Control for Agricultural
Robots via LiDAR and Camera,
RS(15), No. 22, 2023, pp. 5402.
DOI Link
2311
BibRef
Gong, B.[Bowen],
Sun, J.[Jinghang],
Lin, C.[Ciyun],
Liu, H.C.[Hong-Chao],
Sun, G.[Ganghao],
Louvain-Based Traffic Object Detection for Roadside 4D
Millimeter-Wave Radar,
RS(16), No. 2, 2024, pp. 366.
DOI Link
2402
BibRef
Thanh, P.T.H.[Phan Thi Huyen],
Bui, M.Q.V.[Minh Quan Viet],
Nguyen, D.D.[Duc Dung],
Pham, T.V.[Tran Vu],
Duy, T.V.T.[Truong Vinh Truong],
Naotake, N.[Natori],
Transfer multi-source knowledge via scale-aware online domain
adaptation in depth estimation for autonomous driving,
IVC(141), 2024, pp. 104871.
Elsevier DOI
2402
Monocular depth estimation, Multi-source domain adaptation,
Meta-learning, Online domain adaptation, Virtual-to-real, Autonomous driving
BibRef
Wang, L.Y.[Lu-Yang],
Lan, J.H.[Jin-Hui],
Li, M.[Min],
AFRNet: Anchor-Free Object Detection Using Roadside LiDAR in Urban
Scenes,
RS(16), No. 5, 2024, pp. 782.
DOI Link
2403
BibRef
Liu, J.Q.[Jian-Qi],
Zhao, J.G.[Jian-Guo],
Guo, J.F.[Jun-Feng],
Zou, C.F.[Cai-Feng],
Yin, X.W.[Xiu-Wen],
Cheng, X.C.[Xiao-Chun],
Khan, F.[Fazlullah],
Automatic Background Filtering for Cooperative Perception Using
Roadside LiDAR,
ITS(25), No. 7, July 2024, pp. 6964-6977.
IEEE DOI
2407
Filtering, Laser radar, Sensors, Laser beams, Real-time systems,
Task analysis, Automatic background filtering, frame selection,
space division
BibRef
Zhang, T.Y.T.[Tian-Ya Terry],
Ge, Y.[Yi],
Chen, A.[Anjiang],
Sartipi, M.[Mina],
Jin, P.J.[Peter J.],
Hash-Based Gaussian Mixture Model (HGMM) for Roadside LiDAR Smart
Infrastructure Applications,
ITS(25), No. 10, October 2024, pp. 12968-12979.
IEEE DOI
2410
Laser radar, Laser beams, Point cloud compression,
Object detection, Data models, Solid modeling, Hashing algorithm,
smart infrastructure application
BibRef
Du, R.H.[Rong-Hua],
Feng, R.[Rongying],
Gao, K.[Kai],
Zhang, J.[Jinlai],
Liu, L.H.[Lin-Hong],
Self-Supervised Point Cloud Prediction for Autonomous Driving,
ITS(25), No. 11, November 2024, pp. 17452-17467.
IEEE DOI
2411
Point cloud compression, Trajectory, Predictive models,
Prediction algorithms, Solid modeling, Estimation, trajectory
BibRef
Yataka, R.[Ryoma],
Wang, P.[Pu],
Boufounos, P.[Petros],
Takahashi, R.[Ryuhei],
SIRA: Scalable Inter-Frame Relation and Association for Radar
Perception,
CVPR24(15024-15034)
IEEE DOI
2410
Scalability, Radar detection, Radar, Object detection,
Radar tracking, Transformers, Reflection, autonomous driving, ADAS,
deep neural network
BibRef
Ma, J.[Junyi],
Chen, X.[Xieyuanli],
Huang, J.W.[Jia-Wei],
Xu, J.Y.[Jing-Yi],
Luo, Z.[Zhen],
Xu, J.T.[Jin-Tao],
Gu, W.H.[Wei-Hao],
Ai, R.[Rui],
Wang, H.S.[He-Sheng],
Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in
Autonomous Driving Applications,
CVPR24(21486-21495)
IEEE DOI Code:
WWW Link.
2410
Protocols, Estimation, Training data, Benchmark testing,
Predictive models, Spatiotemporal phenomena, occupancy forecasting
BibRef
Ouasfi, A.[Amine],
Boukhayma, A.[Adnane],
Unsupervised Occupancy Learning from Sparse Point Cloud,
CVPR24(21729-21739)
IEEE DOI
2410
Point cloud compression, Training, Uncertainty, Shape,
Measurement uncertainty, Entropy
BibRef
Ma, Q.H.[Qi-Hang],
Tan, X.[Xin],
Qu, Y.[Yanyun],
Ma, L.Z.[Li-Zhuang],
Zhang, Z.Z.[Zhi-Zhong],
Xie, Y.[Yuan],
COTR: Compact Occupancy TRansformer for Vision-Based 3D Occupancy
Prediction,
CVPR24(19936-19945)
IEEE DOI Code:
WWW Link.
2410
Geometry, Semantics, Performance gain, Transformers, Decoding
BibRef
Agro, B.[Ben],
Sykora, Q.[Quinlan],
Casas, S.[Sergio],
Gilles, T.[Thomas],
Urtasun, R.[Raquel],
UnO: Unsupervised Occupancy Fields for Perception and Forecasting,
CVPR24(14487-14496)
IEEE DOI
2410
Point cloud compression, Roads, Semantics, Predictive models,
Data models, Trajectory, Spatiotemporal phenomena, Unsupervised
BibRef
Yang, Z.[Zetong],
Chen, L.[Li],
Sun, Y.[Yanan],
Li, H.Y.[Hong-Yang],
Visual Point Cloud Forecasting Enables Scalable Autonomous Driving,
CVPR24(14673-14684)
IEEE DOI
2410
Point cloud compression, Visualization, Scalability, Semantics,
Predictive models, Rendering (computer graphics)
BibRef
Kälble, J.[Jonas],
Wirges, S.[Sascha],
Tatarchenko, M.[Maxim],
Ilg, E.[Eddy],
Accurate Training Data for Occupancy Map Prediction in Automated
Driving Using Evidence Theory,
CVPR24(5281-5290)
IEEE DOI
2410
Training, Geometry, Point cloud compression, Laser radar, Accuracy,
Evidence theory, Occupancy Prediction, Automated Driving
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Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Road Scene, General Analysis .