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computer-vision-7

RGB-D Reconstruction

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Efficient Plane-Based Optimization of Geometry and Texture for Indoor RGB-D Reconstruction

Chao Wang, Xiaohu Guo•Mon May 20 2019

This approach takes as input a RGB-D sequence and a dense coarse mesh reconstructed from it, and generates a lightweight, low-polygonal mesh with clear face textures and sharp features without losing geometry details from the original scene.

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DeepDeform: Learning Non-Rigid RGB-D Reconstruction With Semi-Supervised Data

M. Nießner, M. Zollhöfer, C. Theobalt, Aljaz Bozic•Sun Dec 08 2019

A new neural network that operates on RGB-D frames is proposed that significantly outperforms existing non-rigid reconstruction methods that do not use learned data terms, as well as learning-based approaches that only use self-supervision.

111 0

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Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation

Kai Xu, Jiazhao Zhang, Chenyang Zhu, Lintao Zheng•Thu Mar 12 2020

A novel fusion-aware 3D point convolution which operates directly on the geometric surface being reconstructed and exploits effectively the inter-frame correlation for high-quality 3D feature learning is proposed.

68 0

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Depth-supervised NeRF: Fewer Views and Faster Training for Free

Deva Ramanan, Andrew Liu, Kangle Deng, Jun-Yan Zhu•Mon Jul 05 2021

This work formalizes the above assumption through DS-NeRF (Depth-supervised Neural Radiance Fields), a loss for learning radiance fields that takes advantage of readily-available depth supervision and can render better images given fewer training views while training 2-3x faster.

1060 0

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Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera

Juyong Zhang, Hongrui Cai, Wanquan Feng, Xuetao Feng, Yan Wang•Wed Jun 29 2022

Neural-DynamicReconstruction (NDR), a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera, is proposed and demonstrates that NDR outperforms existing monocular dynamic reconstruction methods.

78 0

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LiveNVS: Neural View Synthesis on Live RGB-D Streams

Laura Fink, Darius Rückert, Linus Franke, Joachim Keinert, Marc Stamminger•Mon Nov 27 2023

This paper presents LiveNVS, a system that allows for neural novel view synthesis on a live RGB-D input stream with very low latency and real-time rendering, and achieves state-of-the-art neural rendering quality of unknown scenes during capturing, allowing users to virtually explore the scene and assess reconstruction quality in real- time.

8 0

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High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF

D. Cremers, Lu Sang, Bjoern Haefner, Xingxing Zuo•Thu Oct 20 2022

A novel multi-view RGB-D based reconstruction method that tackles camera pose, lighting, albedo, and surface normal estimation via the utilization of a gradient signed distance field (gradient-SDF).

11 0

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RGB-D Reconstruction | State-of-the-Art