Search Results for author: Changjiang Cai

Found 8 papers, 3 papers with code

Dynamic Voxel Grid Optimization for High-Fidelity RGB-D Supervised Surface Reconstruction

no code implementations12 Apr 2023 Xiangyu Xu, Lichang Chen, Changjiang Cai, Huangying Zhan, Qingan Yan, Pan Ji, Junsong Yuan, Heng Huang, Yi Xu

Direct optimization of interpolated features on multi-resolution voxel grids has emerged as a more efficient alternative to MLP-like modules.

Surface Reconstruction

CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation

no code implementations25 Oct 2022 Zhiqi Zhang, Nitin Bansal, Changjiang Cai, Pan Ji, Qingan Yan, Xiangyu Xu, Yi Xu

To this end, we propose CLIP-FLow, a semi-supervised iterative pseudo-labeling framework to transfer the pretraining knowledge to the target real domain.

Contrastive Learning Optical Flow Estimation +1

RIAV-MVS: Recurrent-Indexing an Asymmetric Volume for Multi-View Stereo

no code implementations CVPR 2023 Changjiang Cai, Pan Ji, Qingan Yan, Yi Xu

At the pixel level, we propose to break the symmetry of the Siamese network (which is typically used in MVS to extract image features) by introducing a transformer block to the reference image (but not to the source images).

Depth Estimation

PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo

no code implementations CVPR 2022 Jiachen Liu, Pan Ji, Nitin Bansal, Changjiang Cai, Qingan Yan, Xiaolei Huang, Yi Xu

The semantic plane detection branch is based on a single-view plane detection framework but with differences.

3D Reconstruction

Do End-to-end Stereo Algorithms Under-utilize Information?

1 code implementation14 Oct 2020 Changjiang Cai, Philippos Mordohai

In this paper, we show how deep adaptive filtering and differentiable semi-global aggregation can be integrated in existing 2D and 3D convolutional networks for end-to-end stereo matching, leading to improved accuracy.

Disparity Estimation Stereo Matching

CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation

1 code implementation CVPR 2018 Konstantinos Batsos, Changjiang Cai, Philippos Mordohai

The success of these methods is due to the availability of training data with ground truth; training learning-based systems on these datasets has allowed them to surpass the accuracy of conventional approaches based on heuristics and assumptions.

Disparity Estimation Stereo Matching +1

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