Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Huo, Yuchi"'
LiDAR simulation plays a crucial role in closed-loop simulation for autonomous driving. Although recent advancements, such as the use of reconstructed mesh and Neural Radiance Fields (NeRF), have made progress in simulating the physical properties of
Externí odkaz:
http://arxiv.org/abs/2410.05111
Publikováno v:
ACCV2024
Multi-task-learning(MTL) is a multi-target optimization task. Neural networks try to realize each target using a shared interpretative space within MTL. However, as the scale of datasets expands and the complexity of tasks increases, knowledge sharin
Externí odkaz:
http://arxiv.org/abs/2410.03778
Recent advancements in sensor technology and deep learning have led to significant progress in 3D human body reconstruction. However, most existing approaches rely on data from a specific sensor, which can be unreliable due to the inherent limitation
Externí odkaz:
http://arxiv.org/abs/2409.04851
Existing open-set recognition (OSR) studies typically assume that each image contains only one class label, and the unknown test set (negative) has a disjoint label space from the known test set (positive), a scenario termed full-label shift. This pa
Externí odkaz:
http://arxiv.org/abs/2407.02386
We present MIRReS, a novel two-stage inverse rendering framework that jointly reconstructs and optimizes the explicit geometry, material, and lighting from multi-view images. Unlike previous methods that rely on implicit irradiance fields or simplifi
Externí odkaz:
http://arxiv.org/abs/2406.16360
Generating reliable pseudo masks from image-level labels is challenging in the weakly supervised semantic segmentation (WSSS) task due to the lack of spatial information. Prevalent class activation map (CAM)-based solutions are challenged to discrimi
Externí odkaz:
http://arxiv.org/abs/2406.15755
This paper presents a novel method designed to enhance the efficiency and accuracy of both image retrieval and pixel retrieval. Traditional diffusion methods struggle to propagate spatial information effectively in conventional graphs due to their re
Externí odkaz:
http://arxiv.org/abs/2406.11242
Autor:
Xie, Rengan, Zheng, Wenting, Huang, Kai, Chen, Yizheng, Wang, Qi, Ye, Qi, Chen, Wei, Huo, Yuchi
Previous efforts have managed to generate production-ready 3D assets from text or images. However, these methods primarily employ NeRF or 3D Gaussian representations, which are not adept at producing smooth, high-quality geometries required by modern
Externí odkaz:
http://arxiv.org/abs/2405.14580
Recently, implicit neural representation has been widely used to generate animatable human avatars. However, the materials and geometry of those representations are coupled in the neural network and hard to edit, which hinders their application in tr
Externí odkaz:
http://arxiv.org/abs/2405.11270
Neural radiance field (NeRF) has achieved impressive results in high-quality 3D scene reconstruction. However, NeRF heavily relies on precise camera poses. While recent works like BARF have introduced camera pose optimization within NeRF, their appli
Externí odkaz:
http://arxiv.org/abs/2404.13896