1 code implementation • 28 Mar 2024 • Chongjie Ye, Yinyu Nie, Jiahao Chang, Yuantao Chen, YiHao Zhi, Xiaoguang Han
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.
2 code implementations • 15 Dec 2023 • Ruijie Zhu, Jiahao Chang, Ziyang Song, Jiahuan Yu, Tianzhu Zhang
This report describes the solution that secured the first place in the "View Synthesis Challenge for Human Heads (VSCHH)" at the ICCV 2023 workshop.
1 code implementation • 10 Oct 2023 • Dongming Wu, Jiahao Chang, Fan Jia, Yingfei Liu, Tiancai Wang, Jianbing Shen
Further, we propose TopoMLP, a simple yet high-performance pipeline for driving topology reasoning.
Ranked #3 on 3D Lane Detection on OpenLane-V2 val
1 code implementation • 16 Jun 2023 • Dongming Wu, Fan Jia, Jiahao Chang, Zhuoling Li, Jianjian Sun, Chunrui Han, Shuailin Li, Yingfei Liu, Zheng Ge, Tiancai Wang
We present the 1st-place solution of OpenLane Topology in Autonomous Driving Challenge.
no code implementations • 29 Mar 2023 • Jiahao Chang, Jiahuan Yu, Tianzhu Zhang
Local feature matching is challenging due to textureless and repetitive patterns.
no code implementations • CVPR 2023 • Jiahuan Yu, Jiahao Chang, Jianfeng He, Tianzhu Zhang, Feng Wu
To deal with the above issues, we propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching, which jointly models the local consistency and scale variations in a unified coarse-to-fine architecture.
no code implementations • CVPR 2023 • Shuo Wang, Xinhai Zhao, Hai-Ming Xu, Zehui Chen, Dameng Yu, Jiahao Chang, Zhen Yang, Feng Zhao
Based on the covariate shift assumption, we find that the gap mainly attributes to the feature distribution of BEV, which is determined by the quality of both depth estimation and 2D image's feature representation.
no code implementations • CVPR 2023 • Zizheng Yang, Jie Huang, Jiahao Chang, Man Zhou, Hu Yu, Jinghao Zhang, Feng Zhao
Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images.
no code implementations • ICCV 2023 • Jiahao Chang, Shuo Wang, HaiMing Xu, Zehui Chen, Chenhongyi Yang, Feng Zhao
Next, we propose a target-aware feature distillation to help the student model learn from the object-centric features of the teacher model.