Zobrazeno 1 - 10
of 549
pro vyhledávání: '"Zhang, Feihu"'
Autor:
Wu, Shuang, Lin, Youtian, Zhang, Feihu, Zeng, Yifei, Xu, Jingxi, Torr, Philip, Cao, Xun, Yao, Yao
Generating high-quality 3D assets from text and images has long been challenging, primarily due to the absence of scalable 3D representations capable of capturing intricate geometry distributions. In this work, we introduce Direct3D, a native 3D gene
Externí odkaz:
http://arxiv.org/abs/2405.14832
Autor:
Li, Peng, Liu, Yuan, Long, Xiaoxiao, Zhang, Feihu, Lin, Cheng, Li, Mengfei, Qi, Xingqun, Zhang, Shanghang, Luo, Wenhan, Tan, Ping, Wang, Wenping, Liu, Qifeng, Guo, Yike
In this paper, we introduce Era3D, a novel multiview diffusion method that generates high-resolution multiview images from a single-view image. Despite significant advancements in multiview generation, existing methods still suffer from camera prior
Externí odkaz:
http://arxiv.org/abs/2405.11616
Autonomous driving simulation system plays a crucial role in enhancing self-driving data and simulating complex and rare traffic scenarios, ensuring navigation safety. However, traditional simulation systems, which often heavily rely on manual modeli
Externí odkaz:
http://arxiv.org/abs/2402.02112
Point cloud registration is challenging in the presence of heavy outlier correspondences. This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice. The gravity directions a
Externí odkaz:
http://arxiv.org/abs/2311.01432
Publikováno v:
Open Chemistry, Vol 22, Iss 1, Pp 32-41 (2024)
Sepsis is a severe reaction of the body to an infection, presenting a critical medical crisis. It represents an imbalance between the body’s anti- and pro-inflammatory reactions. The occurrence of sepsis, which leads to multiple-organ failure and i
Externí odkaz:
https://doaj.org/article/9ae08a097d6b47d3a981207ded7646cb
Publikováno v:
IEEE Transactions on Intelligent Vehicles, 2024
Estimating the rigid transformation between two LiDAR scans through putative 3D correspondences is a typical point cloud registration paradigm. Current 3D feature matching approaches commonly lead to numerous outlier correspondences, making outlier-r
Externí odkaz:
http://arxiv.org/abs/2305.11716
Labeling LiDAR point clouds for training autonomous driving is extremely expensive and difficult. LiDAR simulation aims at generating realistic LiDAR data with labels for training and verifying self-driving algorithms more efficiently. Recently, Neur
Externí odkaz:
http://arxiv.org/abs/2304.14811
Neural Radiance Fields (NeRF) have been proposed for photorealistic novel view rendering. However, it requires many different views of one scene for training. Moreover, it has poor generalizations to new scenes and requires retraining or fine-tuning
Externí odkaz:
http://arxiv.org/abs/2303.09952
Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this paradigm does not fit the nature of the street views that are collected by
Externí odkaz:
http://arxiv.org/abs/2303.00749