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20192022
most citedDepth Completion from Sparse LiDAR Data with Depth-Normal Constraints

16 citations · 42 across the 10 of their papers we have counts for

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cs.CV20221 cited

NeuralMarker: A Framework for Learning General Marker Correspondence

Zhaoyang Huang, Xiaokun Pan, Weihong Pan +5

We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker. Conventionally, this problem is addresse…

cs.CV20222 cited

RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization

Yan Xu, Kwan-Yee Lin, Guofeng Zhang +2

6-DoF object pose estimation from a monocular image is challenging, and a post-refinement procedure is generally needed for high-precision estimation. In this paper, we propose a f…

cs.CV20222 cited

Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error Modeling

Yan Xu, Junyi Lin, Jianping Shi +3

The correct ego-motion estimation basically relies on the understanding of correspondences between adjacent LiDAR scans. However, given the complex scenarios and the low-resolution…

cs.CV2021

VS-Net: Voting with Segmentation for Visual Localization

Zhaoyang Huang, Han Zhou, Yijin Li +6

Visual localization is of great importance in robotics and computer vision. Recently, scene coordinate regression based methods have shown good performance in visual localization i…

cs.CV20215 cited

LIFE: Lighting Invariant Flow Estimation

Zhaoyang Huang, Xiaokun Pan, Runsen Xu +4

We tackle the problem of estimating flow between two images with large lighting variations. Recent learning-based flow estimation frameworks have shown remarkable performance on im…

cs.CV20213 cited

Wide-Baseline Multi-Camera Calibration using Person Re-Identification

Yan Xu, Yu-Jhe Li, Xinshuo Weng +1

We address the problem of estimating the 3D pose of a network of cameras for large-environment wide-baseline scenarios, e.g., cameras for construction sites, sports stadiums, and p…