Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Feng, Bailan"'
This paper studies point cloud perception within outdoor environments. Existing methods face limitations in recognizing objects located at a distance or occluded, due to the sparse nature of outdoor point clouds. In this work, we observe a significan
Externí odkaz:
http://arxiv.org/abs/2411.07742
Vision-centric semantic occupancy prediction plays a crucial role in autonomous driving, which requires accurate and reliable predictions from low-cost sensors. Although having notably narrowed the accuracy gap with LiDAR, there is still few research
Externí odkaz:
http://arxiv.org/abs/2409.18026
Autor:
Zheng, Jilai, Tang, Pin, Wang, Zhongdao, Wang, Guoqing, Ren, Xiangxuan, Feng, Bailan, Ma, Chao
Perceiving the world as 3D occupancy supports embodied agents to avoid collision with any types of obstacle. While open-vocabulary image understanding has prospered recently, how to bind the predicted 3D occupancy grids with open-world semantics stil
Externí odkaz:
http://arxiv.org/abs/2407.12294
Autor:
Li, Jianhao, Sun, Tianyu, Wang, Zhongdao, Xie, Enze, Feng, Bailan, Zhang, Hongbo, Yuan, Ze, Xu, Ke, Liu, Jiaheng, Luo, Ping
This paper proposes an algorithm for automatically labeling 3D objects from 2D point or box prompts, especially focusing on applications in autonomous driving. Unlike previous arts, our auto-labeler predicts 3D shapes instead of bounding boxes and do
Externí odkaz:
http://arxiv.org/abs/2407.11382
Autor:
Wang, Guoqing, Wang, Zhongdao, Tang, Pin, Zheng, Jilai, Ren, Xiangxuan, Feng, Bailan, Ma, Chao
Existing solutions for 3D semantic occupancy prediction typically treat the task as a one-shot 3D voxel-wise segmentation perception problem. These discriminative methods focus on learning the mapping between the inputs and occupancy map in a single
Externí odkaz:
http://arxiv.org/abs/2404.15014
Autor:
Tang, Pin, Wang, Zhongdao, Wang, Guoqing, Zheng, Jilai, Ren, Xiangxuan, Feng, Bailan, Ma, Chao
Publikováno v:
IEEE Conference on Computer Vision and Pattern Recognition 2024 (CVPR 2024)
Vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied. However, operating on dense latent spaces introduces a cubic time and spac
Externí odkaz:
http://arxiv.org/abs/2404.09502
Autor:
Zhao, Qiang, Chen, Bin, Xu, Hang, Ma, Yike, Li, Xiaodong, Feng, Bailan, Yan, Chenggang, Dai, Feng
As one of the most fundamental and challenging problems in computer vision, object detection tries to locate object instances and find their categories in natural images. The most important step in the evaluation of object detection algorithm is calc
Externí odkaz:
http://arxiv.org/abs/2108.08029
Publikováno v:
In Neurocomputing 25 August 2021 450:219-229
Akademický článek
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Publikováno v:
ICME Workshops
Learning to recognize pedestrian attributes (such as gender, hair style, take hat or not) in video surveillance scenarios is critical to a variety of tasks, such as crime prevention and border control. However, it is still challenging due to low reso
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6136e36a5233c1a7ed4b46964b803b23
https://doi.org/10.1109/icmew.2017.8026292
https://doi.org/10.1109/icmew.2017.8026292