no code implementations • 9 Oct 2024 • Runze Chen, Haiyong Luo, Fang Zhao, Jingze Yu, Yupeng Jia, Juan Wang, Xuepeng Ma
Our approach tackles the non-robustness of existing self-supervised monocular depth estimation models to interference textures by adopting a structure-centered perspective and utilizing the scene structure characteristics demonstrated by semantics and illumination.
no code implementations • 13 Sep 2024 • Runze Chen, Mingyu Xiao, Haiyong Luo, Fang Zhao, Fan Wu, Hao Xiong, Qi Liu, Meng Song
We introduce Crowd-Sourced Splatting (CSS), a novel 3D Gaussian Splatting (3DGS) pipeline designed to overcome the challenges of pose-free scene reconstruction using crowd-sourced imagery.
no code implementations • 23 Aug 2024 • Mingyu Xiao, Runze Chen, Haiyong Luo, Fang Zhao, Juan Wang, Xuepeng Ma
Due to the inherent limitations of the camera itself, recovering the metric scale from a single image is crucial, as this significantly impacts the translation error.
no code implementations • 15 Sep 2023 • Yupeng Jia, Jie He, Runze Chen, Fang Zhao, Haiyong Luo
Subsequently, queries for both foreground and background objects are fed into the mixed dense-sparse 3D occupancy decoder, performing upsampling in dense and sparse methods, respectively.
1 code implementation • 27 Jul 2023 • Lingdong Kong, Yaru Niu, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit R. Cottereau, Liangjun Zhang, Hesheng Wang, Wei Tsang Ooi, Ruijie Zhu, Ziyang Song, Li Liu, Tianzhu Zhang, Jun Yu, Mohan Jing, Pengwei Li, Xiaohua Qi, Cheng Jin, Yingfeng Chen, Jie Hou, Jie Zhang, Zhen Kan, Qiang Ling, Liang Peng, Minglei Li, Di Xu, Changpeng Yang, Yuanqi Yao, Gang Wu, Jian Kuai, Xianming Liu, Junjun Jiang, Jiamian Huang, Baojun Li, Jiale Chen, Shuang Zhang, Sun Ao, Zhenyu Li, Runze Chen, Haiyong Luo, Fang Zhao, Jingze Yu
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
no code implementations • 6 Jul 2021 • Runze Chen, Haiyong Luo, Fang Zhao, Xuechun Meng, Zhiqing Xie, Yida Zhu
The comparative experiments of knowledge distillation on six public datasets also demonstrate that the SMLDist outperforms other advanced knowledge distillation methods of students' performance, which verifies the good generalization of the SMLDist on other classification tasks, including but not limited to HAR.
no code implementations • 3 Jan 2019 • Jie Wen, Zuofeng Zhong, Zheng Zhang, Lunke Fei, Zhihui Lai, Runze Chen
This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification.