Video Supp Fast Monocular Scene Reconstruction With Global Sparse Local Dense Grids

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Key Details
Developments
Detailed Analysis
Conclusion

About on Video Supp Fast Monocular Scene Reconstruction With Global Sparse Local Dense Grids

Details [Video Supp] Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids News

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Key Details

Full [Slides] Fast Monocular Scene Reconstruction from Global-Sparse Local-Dense Grids News

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Developments

Information Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion Update

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MOSAIC-GS: Monocular Scene Reconstruction for Complex Dynamic Environments

Dense Reconstruction of Non-Rigid Surfaces from Monocular Video

SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields

Real-time Dense Reconstruction with a Monocular Camera

DeepLiDARFlow: Deep Scene Flow Estimation Using a Monocular Camera and a Sparse LiDAR - IROS 2020

Beyond Dense Sensing: Biomechanically-Guided Hand Pose Reconstruction from Reduce... (Video Preview)

Dense monocular 3D object reconstruction from video

Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction

Monocular Dense 3D Reconstruction of Complex Dynamic Scene from Two Perspective Frames, ICCV 2017

Monocular Reconstruction - Synthetic Sequence

Mesh4D: 4D Mesh Reconstruction and Tracking from Monocular Video

Detailed Analysis

Data is compiled from public records and verified media reports.

Last Updated: October 27, 2026

Conclusion

CVPR 2023 Paper: VisFusion - Visibility-aware Online 3D Scene Reconstruction from Videos (Gao et al) Update

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Summary

Poster session: Tue 20 Jun 7:30 p.m. EDT — 9 p.m. EDT. VisFusion: Visibility-aware Online 3D Meet MOSAIC-GS: the novel framework for efficient Dynamic This are the results of 3D reconstructions obtained with our work presented in CVPR 2013 as an oral. ' A research project in progress in our lab. This project is for The first method that employs deformation field into 3d Gaussian splatting for dynamic Author: Suryansh Kumar, Yuchao Dai, Hongdong Li. This work got accepted for publication at IEEE/CVF ICCV 2017.

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