Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Chang, Jiahao"'
Point cloud registration is a fundamental problem for large-scale 3D scene scanning and reconstruction. With the help of deep learning, registration methods have evolved significantly, reaching a nearly-mature stage. As the introduction of Neural Rad
Externí odkaz:
http://arxiv.org/abs/2407.05254
We present GauStudio, a novel modular framework for modeling 3D Gaussian Splatting (3DGS) to provide standardized, plug-and-play components for users to easily customize and implement a 3DGS pipeline. Supported by our framework, we propose a hybrid G
Externí odkaz:
http://arxiv.org/abs/2403.19632
This report describes the solution that secured the first place in the "View Synthesis Challenge for Human Heads (VSCHH)" at the ICCV 2023 workshop. Given the sparse view images of human heads, the objective of this challenge is to synthesize images
Externí odkaz:
http://arxiv.org/abs/2312.09527
Topology reasoning aims to comprehensively understand road scenes and present drivable routes in autonomous driving. It requires detecting road centerlines (lane) and traffic elements, further reasoning their topology relationship, i.e., lane-lane to
Externí odkaz:
http://arxiv.org/abs/2310.06753
Autor:
Wu, Dongming, Jia, Fan, Chang, Jiahao, Li, Zhuoling, Sun, Jianjian, Han, Chunrui, Li, Shuailin, Liu, Yingfei, Ge, Zheng, Wang, Tiancai
We present the 1st-place solution of OpenLane Topology in Autonomous Driving Challenge. Considering that topology reasoning is based on centerline detection and traffic element detection, we develop a multi-stage framework for high performance. Speci
Externí odkaz:
http://arxiv.org/abs/2306.09590
Local feature matching is challenging due to textureless and repetitive patterns. Existing methods focus on using appearance features and global interaction and matching, while the importance of geometry priors in local feature matching has not been
Externí odkaz:
http://arxiv.org/abs/2303.16646
Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency. Meanwhile
Externí odkaz:
http://arxiv.org/abs/2303.16624
Autor:
Wang, Shuo, Zhao, Xinhai, Xu, Hai-Ming, Chen, Zehui, Yu, Dameng, Chang, Jiahao, Yang, Zhen, Zhao, Feng
Multi-view 3D object detection (MV3D-Det) in Bird-Eye-View (BEV) has drawn extensive attention due to its low cost and high efficiency. Although new algorithms for camera-only 3D object detection have been continuously proposed, most of them may risk
Externí odkaz:
http://arxiv.org/abs/2303.01686
Transformer-based detectors (DETRs) are becoming popular for their simple framework, but the large model size and heavy time consumption hinder their deployment in the real world. While knowledge distillation (KD) can be an appealing technique to com
Externí odkaz:
http://arxiv.org/abs/2211.10156
Autor:
Chen, Jia, Zhang, Guoqiang, Wu, Zhongtao, Wu, Quanxin, Chang, Jiahao, Liang, Qikai, Zhang, Lei, Luo, Xiliang, Liu, Yun, Zeng, Weishen
Publikováno v:
In International Journal of Biological Macromolecules August 2024 275 Part 1