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pro vyhledávání: '"Peng, Chensheng"'
Autor:
Wang, Yixiao, Tang, Chen, Sun, Lingfeng, Rossi, Simone, Xie, Yichen, Peng, Chensheng, Hannagan, Thomas, Sabatini, Stefano, Poerio, Nicola, Tomizuka, Masayoshi, Zhan, Wei
Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving, but they face challenges of inefficient inference steps and high computational demands. To tackle these challenges, we introduce Optimal
Externí odkaz:
http://arxiv.org/abs/2408.00766
Autor:
Lin, Haotian, Wang, Yixiao, Huo, Mingxiao, Peng, Chensheng, Liu, Zhiyuan, Tomizuka, Masayoshi
Joint pedestrian trajectory prediction has long grappled with the inherent unpredictability of human behaviors. Recent investigations employing variants of conditional diffusion models in trajectory prediction have exhibited notable success. Neverthe
Externí odkaz:
http://arxiv.org/abs/2404.00237
Autor:
Liu, Jiuming, Zhuo, Dong, Feng, Zhiheng, Zhu, Siting, Peng, Chensheng, Liu, Zhe, Wang, Hesheng
Information inside visual and LiDAR data is well complementary derived from the fine-grained texture of images and massive geometric information in point clouds. However, it remains challenging to explore effective visual-LiDAR fusion, mainly due to
Externí odkaz:
http://arxiv.org/abs/2403.18274
Autor:
Peng, Chensheng, Zeng, Zhaoyu, Gao, Jinling, Zhou, Jundong, Tomizuka, Masayoshi, Wang, Xinbing, Zhou, Chenghu, Ye, Nanyang
Publikováno v:
IEEE Robotics and Automation Letters, 2024
Multiple object tracking is a critical task in autonomous driving. Existing works primarily focus on the heuristic design of neural networks to obtain high accuracy. As tracking accuracy improves, however, neural networks become increasingly complex,
Externí odkaz:
http://arxiv.org/abs/2403.15712
Autor:
Peng, Chensheng, Xu, Chenfeng, Wang, Yue, Ding, Mingyu, Yang, Heng, Tomizuka, Masayoshi, Keutzer, Kurt, Pavone, Marco, Zhan, Wei
Monocular SLAM has long grappled with the challenge of accurately modeling 3D geometries. Recent advances in Neural Radiance Fields (NeRF)-based monocular SLAM have shown promise, yet these methods typically focus on novel view synthesis rather than
Externí odkaz:
http://arxiv.org/abs/2403.08125
Autor:
Peng, Chensheng, Wang, Guangming, Lo, Xian Wan, Wu, Xinrui, Xu, Chenfeng, Tomizuka, Masayoshi, Zhan, Wei, Wang, Hesheng
Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits feature fusion from both modalities for point-wise scene flow estimation. Previous methods rarely predict scene flow from the entire point clouds of the scene w
Externí odkaz:
http://arxiv.org/abs/2308.04383
Multiple object tracking (MOT) is a significant task in achieving autonomous driving. Traditional works attempt to complete this task, either based on point clouds (PC) collected by LiDAR, or based on images captured from cameras. However, relying on
Externí odkaz:
http://arxiv.org/abs/2203.16268
Akademický článek
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