Search Results for author: Ke Xian

Found 13 papers, 4 papers with code

Sparse-to-Dense Depth Completion Revisited: Sampling Strategy and Graph Construction

no code implementations ECCV 2020 Xin Xiong, Haipeng Xiong, Ke Xian, Chen Zhao, Zhiguo Cao, Xin Li

Depth completion is a widely studied problem of predicting a dense depth map from a sparse set of measurements and a single RGB image.

Depth Completion graph construction

Composing Photos Like a Photographer

1 code implementation CVPR 2021 Chaoyi Hong, Shuaiyuan Du, Ke Xian, Hao Lu, Zhiguo Cao, Weicai Zhong

To this end, we introduce the concept of the key composition map (KCM) to encode the composition rules.

Image Cropping

Structure-Guided Ranking Loss for Single Image Depth Prediction

1 code implementation CVPR 2020 Ke Xian, Jianming Zhang, Oliver Wang, Long Mai, Zhe Lin, Zhiguo Cao

Large-scale disparity data generated from stereo photos and 3D videos is a promising source of supervision, however, such disparity data can only approximate the inverse ground truth depth up to an affine transformation.

Affine Transformation Monocular Depth Estimation

Iterative Clustering with Game-Theoretic Matching for Robust Multi-consistency Correspondence

no code implementations3 Sep 2019 Chen Zhao, Jiaqi Yang, Ke Xian, Zhiguo Cao, Xin Li

Matching corresponding features between two images is a fundamental task to computer vision with numerous applications in object recognition, robotics, and 3D reconstruction.

3D Reconstruction Object Recognition

A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching

no code implementations5 Jul 2019 Jiaqi Yang, Ke Xian, Peng Wang, Yanning Zhang

Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision.

3D Object Recognition Point Cloud Registration

Learning to Fuse Local Geometric Features for 3D Rigid Data Matching

no code implementations27 Apr 2019 Jiaqi Yang, Chen Zhao, Ke Xian, Angfan Zhu, Zhiguo Cao

This paper presents a simple yet very effective data-driven approach to fuse both low-level and high-level local geometric features for 3D rigid data matching.

Deep attention-based classification network for robust depth prediction

1 code implementation11 Jul 2018 Ruibo Li, Ke Xian, Chunhua Shen, Zhiguo Cao, Hao Lu, Lingxiao Hang

However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)?

Classification Deep Attention +4

Monocular Depth Estimation with Augmented Ordinal Depth Relationships

no code implementations2 Jun 2018 Yuanzhouhan Cao, Tianqi Zhao, Ke Xian, Chunhua Shen, Zhiguo Cao, Shugong Xu

In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm.

Monocular Depth Estimation Stereo Matching +1

Performance Evaluation of 3D Correspondence Grouping Algorithms

no code implementations6 Apr 2018 Jiaqi Yang, Ke Xian, Yang Xiao, Zhiguo Cao

This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences.

3D Object Recognition Point Cloud Registration

When Unsupervised Domain Adaptation Meets Tensor Representations

1 code implementation ICCV 2017 Hao Lu, Lei Zhang, Zhiguo Cao, Wei Wei, Ke Xian, Chunhua Shen, Anton Van Den Hengel

Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another.

Unsupervised Domain Adaptation

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