作者: CREEK 時間: 2025-3-21 22:00
Liang Zhao,Lingfei Wu,Peng Cui,Jian Pein, including semantic understanding and target detection. These works have designed various strategies to take advantage of fish-eye cameras and prevent image distortions, showing the broad application prospects of fish-eye cameras. The development trend of fish-eye cameras in autonomous driving is discussed.作者: FOLLY 時間: 2025-3-22 01:10 作者: armistice 時間: 2025-3-22 06:54 作者: 使高興 時間: 2025-3-22 09:32
Environmental Perception Using Fish-Eye Cameras for Autonomous Driving,n, including semantic understanding and target detection. These works have designed various strategies to take advantage of fish-eye cameras and prevent image distortions, showing the broad application prospects of fish-eye cameras. The development trend of fish-eye cameras in autonomous driving is discussed.作者: RADE 時間: 2025-3-22 16:45 作者: 煤渣 時間: 2025-3-22 18:52 作者: 低能兒 時間: 2025-3-22 22:56
2191-6586 basics, immersing you in the world of machine vision and intDiscover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary s作者: sed-rate 時間: 2025-3-23 02:25 作者: OREX 時間: 2025-3-23 05:53 作者: 折磨 時間: 2025-3-23 13:46
Book 2023 in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations o作者: Outshine 時間: 2025-3-23 14:48 作者: 完成 時間: 2025-3-23 22:00
Graph Separators, with Applicationshe current state-of-the-art research, datasets, evaluation metrics, and industrial applications. In the end, we outline several existing challenges and present our own conclusions regarding BEV perception.作者: 嘴唇可修剪 時間: 2025-3-24 00:35
,3D Object Detection in?Autonomous Driving,ate-of-the-art techniques and categorize them into camera-based, LiDAR-based, RADAR-based and multi-sensor fusion methods. For each method, we point out the main problems and their existing solutions. By analyzing the limitations of existing methods, we propose promising directions and open problems for future research.作者: 招惹 時間: 2025-3-24 05:40 作者: 火海 時間: 2025-3-24 10:19 作者: CRUE 時間: 2025-3-24 10:45
Environmental Perception Using Fish-Eye Cameras for Autonomous Driving,es a large field of view (FoV). Because of this special feature, the fish-eye camera has abundant applications in environmental perception and autonomous driving. However, many challenges still exist in the practical application of fish-eye cameras. In this chapter, typical fish-eye datasets, includ作者: 低三下四之人 時間: 2025-3-24 16:02 作者: FORGO 時間: 2025-3-24 22:57
Semantic Segmentation for Autonomous Driving,s belonging to the same category using artificial intelligence. This is an important step from image processing to image analysis and has numerous applications in areas such as automatic driving, image enhancement, and 3D map reconstruction. With the emergence of deep learning, several sophisticated作者: conifer 時間: 2025-3-25 01:52 作者: arbovirus 時間: 2025-3-25 06:56
Collaborative 3D Object Detection,individual vehicles results in the bottleneck of improvement of the 3D detection performance. To break through the limits of individual detection, collaborative 3D object detection has been proposed which enables agents to share information to perceive the environments beyond line-of-sight and field作者: 暗指 時間: 2025-3-25 07:44
,Enabling Robust SLAM for?Mobile Robots with?Sensor Fusion,ress in solving the probabilistic SLAM problem by presenting various theoretical frameworks, efficient solvers, and complete systems. As the development of autonomous robots (i.e., self-driving cars, legged robots) continues, SLAM systems have become increasingly popular for large-scale real-world a作者: 慌張 時間: 2025-3-25 12:51 作者: 茁壯成長 時間: 2025-3-25 18:33
Multi-task Perception for Autonomous Driving,, many self-supervised pre-training methods have been proposed and they have achieved impressive performance on a range of computer vision tasks. However, their generalization ability to multi-task scenarios is yet to be explored. Besides, most multi-task algorithms are designed for specific tasks u作者: 緊張過度 時間: 2025-3-25 23:40
,Bird’s Eye View Perception for?Autonomous Driving,, map segmentation, and motion prediction. Due to its inherent advantages in representing 3D space, fusing multi-modal data, facilitating decision making, and aiding in path planning, BEV perception has garnered significant attention from both academia and industry. In this chapter, we present an ov作者: 水獺 時間: 2025-3-26 03:58 作者: Spina-Bifida 時間: 2025-3-26 08:10 作者: 笨重 時間: 2025-3-26 12:20
Background and Traditional Approaches,e summary of evaluation metrics used to assess semantic segmentation results, along with corresponding benchmarks for a number of classic datasets, is also presented. Finally, practical applications of semantic segmentation in autonomous driving are explored, and conclusions are drawn on the current作者: bleach 時間: 2025-3-26 15:49
Traditional Graph Generation Approaches jointly use object features and point features to estimate camera 6-Degrees Of Freedom (6-DOF) poses and do richer map construction. Experiments are conducted using the proposed datasets and criteria with several state-of-the-art VSLAM methods to demonstrate the functionality of our datasets. Owing作者: Cognizance 時間: 2025-3-26 19:13 作者: hermitage 時間: 2025-3-26 21:49 作者: CUB 時間: 2025-3-27 04:22
2191-6586 amples to help reinforce your learning. Don‘t miss out on this essential guide to computer vision and deep learning for autonomous driving.978-981-99-4289-3978-981-99-4287-9Series ISSN 2191-6586 Series E-ISSN 2191-6594 作者: 驚奇 時間: 2025-3-27 06:26 作者: 拋射物 時間: 2025-3-27 12:57
Semantic Segmentation for Autonomous Driving,e summary of evaluation metrics used to assess semantic segmentation results, along with corresponding benchmarks for a number of classic datasets, is also presented. Finally, practical applications of semantic segmentation in autonomous driving are explored, and conclusions are drawn on the current作者: Seminar 時間: 2025-3-27 14:50 作者: Innocence 時間: 2025-3-27 17:59
Multi-task Perception for Autonomous Driving,d effective pretrain-adapt-finetune paradigm for multi-task learning and a novel adapter named LV-Adapter which reuses powerful knowledge from the Contrastive Language-Image Pre-training (CLIP) model pre-trained on image-text pairs. We further present an effective multi-task framework for autonomous作者: 填滿 時間: 2025-3-27 22:39
Road Environment Perception for Safe and Comfortable Driving,tems, envisioning the integration of both vision and motion sensors. We believe that this survey will act as a helpful guide for advancing road defect detection technology, providing strategic advice and practical guidance to those involved in developing such systems.作者: RAG 時間: 2025-3-28 04:15
Rui Fan,Sicen Guo,Mohammud Junaid BocusDiscover the ultimate guide that takes you through the most recent breakthroughs in computer vision.This comprehensive book goes beyond the basics, immersing you in the world of machine vision and int作者: 昏暗 時間: 2025-3-28 08:01
Advances in Computer Vision and Pattern Recognitionhttp://image.papertrans.cn/b/image/166773.jpg作者: 利用 時間: 2025-3-28 12:52 作者: indicate 時間: 2025-3-28 17:25
Liang Zhao,Lingfei Wu,Peng Cui,Jian Peies a large field of view (FoV). Because of this special feature, the fish-eye camera has abundant applications in environmental perception and autonomous driving. However, many challenges still exist in the practical application of fish-eye cameras. In this chapter, typical fish-eye datasets, includ作者: 大酒杯 時間: 2025-3-28 21:25 作者: 完成才能戰(zhàn)勝 時間: 2025-3-29 00:44
Background and Traditional Approaches,s belonging to the same category using artificial intelligence. This is an important step from image processing to image analysis and has numerous applications in areas such as automatic driving, image enhancement, and 3D map reconstruction. With the emergence of deep learning, several sophisticated作者: Aphorism 時間: 2025-3-29 04:29 作者: FLEET 時間: 2025-3-29 08:33
Multi-Relational Data and Knowledge Graphsindividual vehicles results in the bottleneck of improvement of the 3D detection performance. To break through the limits of individual detection, collaborative 3D object detection has been proposed which enables agents to share information to perceive the environments beyond line-of-sight and field作者: 脆弱吧 時間: 2025-3-29 15:23
Background and Traditional Approaches,ress in solving the probabilistic SLAM problem by presenting various theoretical frameworks, efficient solvers, and complete systems. As the development of autonomous robots (i.e., self-driving cars, legged robots) continues, SLAM systems have become increasingly popular for large-scale real-world a作者: TEM 時間: 2025-3-29 17:41
Traditional Graph Generation ApproachesM) systems. However, retrieving ground truth, estimating calibration parameters and annotating useful labels all require cumbersome human labor. Moreover, there are lots of object instances in the environments while traditional mapping modules can only estimate 3D information of isolated sparse or s作者: 生命層 時間: 2025-3-29 22:54
