Zobrazeno 1 - 10
of 2 473
pro vyhledávání: '"Liu Bingbing"'
Autor:
Zhang, Haiming, Xue, Ying, Yan, Xu, Zhang, Jiacheng, Qiu, Weichao, Bai, Dongfeng, Liu, Bingbing, Cui, Shuguang, Li, Zhen
The field of autonomous driving is experiencing a surge of interest in world models, which aim to predict potential future scenarios based on historical observations. In this paper, we introduce DFIT-OccWorld, an efficient 3D occupancy world model th
Externí odkaz:
http://arxiv.org/abs/2412.13772
Autor:
Zhou, Hongyu, Lin, Longzhong, Wang, Jiabao, Lu, Yichong, Bai, Dongfeng, Liu, Bingbing, Wang, Yue, Geiger, Andreas, Liao, Yiyi
In the past few decades, autonomous driving algorithms have made significant progress in perception, planning, and control. However, evaluating individual components does not fully reflect the performance of entire systems, highlighting the need for
Externí odkaz:
http://arxiv.org/abs/2412.01718
Autor:
Xu, Tianshuo, Chen, Zhifei, Wu, Leyi, Lu, Hao, Chen, Yuying, Jiang, Lihui, Liu, Bingbing, Chen, Yingcong
Recent numerous video generation models, also known as world models, have demonstrated the ability to generate plausible real-world videos. However, many studies have shown that these models often produce motion results lacking logical or physical co
Externí odkaz:
http://arxiv.org/abs/2412.00547
This technical report summarizes the second-place solution for the Predictive World Model Challenge held at the CVPR-2024 Workshop on Foundation Models for Autonomous Systems. We introduce D$^2$-World, a novel World model that effectively forecasts f
Externí odkaz:
http://arxiv.org/abs/2411.17027
Autor:
Ren, Yuan, Wu, Guile, Li, Runhao, Yang, Zheyuan, Liu, Yibo, Chen, Xingxin, Cao, Tongtong, Liu, Bingbing
Urban scene reconstruction is crucial for real-world autonomous driving simulators. Although existing methods have achieved photorealistic reconstruction, they mostly focus on pinhole cameras and neglect fisheye cameras. In fact, how to effectively s
Externí odkaz:
http://arxiv.org/abs/2411.15355
Autor:
Zhang, Haiming, Zhou, Wending, Zhu, Yiyao, Yan, Xu, Gao, Jiantao, Bai, Dongfeng, Cai, Yingjie, Liu, Bingbing, Cui, Shuguang, Li, Zhen
This paper introduces VisionPAD, a novel self-supervised pre-training paradigm designed for vision-centric algorithms in autonomous driving. In contrast to previous approaches that employ neural rendering with explicit depth supervision, VisionPAD ut
Externí odkaz:
http://arxiv.org/abs/2411.14716
Autor:
Li, Leheng, Qiu, Weichao, Yan, Xu, He, Jing, Zhou, Kaiqiang, Cai, Yingjie, Lian, Qing, Liu, Bingbing, Chen, Ying-Cong
We present OmniBooth, an image generation framework that enables spatial control with instance-level multi-modal customization. For all instances, the multimodal instruction can be described through text prompts or image references. Given a set of us
Externí odkaz:
http://arxiv.org/abs/2410.04932
The advancement of autonomous driving is increasingly reliant on high-quality annotated datasets, especially in the task of 3D occupancy prediction, where the occupancy labels require dense 3D annotation with significant human effort. In this paper,
Externí odkaz:
http://arxiv.org/abs/2410.00337
Autor:
He, Jing, Li, Haodong, Yin, Wei, Liang, Yixun, Li, Leheng, Zhou, Kaiqiang, Zhang, Hongbo, Liu, Bingbing, Chen, Ying-Cong
Leveraging the visual priors of pre-trained text-to-image diffusion models offers a promising solution to enhance zero-shot generalization in dense prediction tasks. However, existing methods often uncritically use the original diffusion formulation,
Externí odkaz:
http://arxiv.org/abs/2409.18124
Autor:
Miao, Sheng, Huang, Jiaxin, Bai, Dongfeng, Qiu, Weichao, Liu, Bingbing, Geiger, Andreas, Liao, Yiyi
Recent advances in implicit scene representation enable high-fidelity street view novel view synthesis. However, existing methods optimize a neural radiance field for each scene, relying heavily on dense training images and extensive computation reso
Externí odkaz:
http://arxiv.org/abs/2407.12395