no code implementations • 3 Apr 2024 • Cheng Zhao, Su Sun, Ruoyu Wang, Yuliang Guo, Jun-Jun Wan, Zhou Huang, Xinyu Huang, Yingjie Victor Chen, Liu Ren
Most 3D Gaussian Splatting (3D-GS) based methods for urban scenes initialize 3D Gaussians directly with 3D LiDAR points, which not only underutilizes LiDAR data capabilities but also overlooks the potential advantages of fusing LiDAR with camera data.
no code implementations • 3 Apr 2024 • Su Sun, Cheng Zhao, Yuliang Guo, Ruoyu Wang, Xinyu Huang, Yingjie Victor Chen, Liu Ren
The 3D Inpainter with abstract representation at coarse levels is trained offline using various scenes to complete occluded surfaces.
1 code implementation • 29 Mar 2024 • Abhinav Kumar, Yuliang Guo, Xinyu Huang, Liu Ren, Xiaoming Liu
We argue that the cause of failure is the sensitivity of depth regression losses to noise of larger objects.
3D Object Detection 3D Object Detection From Monocular Images +3
no code implementations • 23 Mar 2024 • Yuliang Guo, Abhinav Kumar, Cheng Zhao, Ruoyu Wang, Xinyu Huang, Liu Ren
Monocular 3D reconstruction for categorical objects heavily relies on accurately perceiving each object's pose.
1 code implementation • NeurIPS 2023 • Yunhao Ge, Hong-Xing Yu, Cheng Zhao, Yuliang Guo, Xinyu Huang, Liu Ren, Laurent Itti, Jiajun Wu
A major challenge in monocular 3D object detection is the limited diversity and quantity of objects in real datasets.
1 code implementation • CVPR 2022 • Yuyan Li, Yuliang Guo, Zhixin Yan, Xinyu Huang, Ye Duan, Liu Ren
In this paper, we propose a 360 monocular depth estimation pipeline, OmniFusion, to tackle the spherical distortion issue.
Ranked #6 on Depth Estimation on Stanford2D3D Panoramic
1 code implementation • 12 Dec 2020 • Yuliang Guo, Zhong Li, Zekun Li, Xiangyu Du, Shuxue Quan, Yi Xu
In this paper, a real-time method called PoP-Net is proposed to predict multi-person 3D poses from a depth image.
1 code implementation • ECCV 2020 • Yuliang Guo, Guang Chen, Peitao Zhao, Weide Zhang, Jinghao Miao, Jingao Wang, Tae Eun Choe
The method, inspired by the latest state-of-the-art 3D-LaneNet, is a unified framework solving image encoding, spatial transform of features and 3D lane prediction in a single network.
Ranked #8 on 3D Lane Detection on Apollo Synthetic 3D Lane
no code implementations • 28 Jun 2018 • Yuliang Guo, Lakshmi Narasimhan Govindarajan, Benjamin Kimia, Thomas Serre
We present a novel approach for estimating the 2D pose of an articulated object with an application to automated video analysis of small laboratory animals.