Search Results for author: Ke Xian

Found 27 papers, 11 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

DyBluRF: Dynamic Neural Radiance Fields from Blurry Monocular Video

no code implementations15 Mar 2024 Huiqiang Sun, Xingyi Li, Liao Shen, Xinyi Ye, Ke Xian, Zhiguo Cao

Experimental results on our dataset demonstrate that our method outperforms existing approaches in generating sharp novel views from motion-blurred inputs while maintaining spatial-temporal consistency of the scene.

S-DyRF: Reference-Based Stylized Radiance Fields for Dynamic Scenes

no code implementations10 Mar 2024 Xingyi Li, Zhiguo Cao, Yizheng Wu, Kewei Wang, Ke Xian, Zhe Wang, Guosheng Lin

To address this limitation, we present S-DyRF, a reference-based spatio-temporal stylization method for dynamic neural radiance fields.

Style Transfer

Make-It-4D: Synthesizing a Consistent Long-Term Dynamic Scene Video from a Single Image

no code implementations20 Aug 2023 Liao Shen, Xingyi Li, Huiqiang Sun, Juewen Peng, Ke Xian, Zhiguo Cao, Guosheng Lin

To animate the visual content, the feature point cloud is displaced based on the scene flow derived from motion estimation and the corresponding camera pose.

Motion Estimation

Diffusion-Augmented Depth Prediction with Sparse Annotations

no code implementations4 Aug 2023 Jiaqi Li, Yiran Wang, Zihao Huang, Jinghong Zheng, Ke Xian, Zhiguo Cao, Jianming Zhang

We leverage the structural characteristics of diffusion model to enforce depth structures of depth models in a plug-and-play manner.

Autonomous Driving Depth Estimation +3

Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors

no code implementations7 Jun 2023 Xianrui Luo, Juewen Peng, Ke Xian, Zijin Wu, Zhiguo Cao

To this end, we present a Defocus to Focus (D2F) framework to learn realistic bokeh rendering by fusing defocus priors with the all-in-focus image and by implementing radiance priors in layered fusion.


Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair

no code implementations11 Apr 2023 Xianrui Luo, Juewen Peng, Weiyue Zhao, Ke Xian, Hao Lu, Zhiguo Cao

Benefiting from the multi-camera module in modern smartphones, we introduce a new task of AIF synthesis from main (wide) and ultra-wide cameras.

MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects

1 code implementation18 Jul 2022 Juewen Peng, Jianming Zhang, Xianrui Luo, Hao Lu, Ke Xian, Zhiguo Cao

Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background.

BokehMe: When Neural Rendering Meets Classical Rendering

1 code implementation CVPR 2022 Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, Jianming Zhang

Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer.

Neural Rendering

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.

Depth Prediction 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 Clustering +2

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 +1

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 +5

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.

Depth Prediction Monocular Depth Estimation +2

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 +1

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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