Neural RGB-D Surface Reconstruction

@article{Azinovic2022NeuralRS,
  title={Neural RGB-D Surface Reconstruction},
  author={Dejan Azinovi'c and Ricardo Martin-Brualla and Dan B. Goldman and Matthias Nie{\ss}ner and Justus Thies},
  journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022},
  pages={6280-6291}
}
Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance for upcoming applications in AR or VR. These range from mixed reality applications for teleconferencing, virtual measuring, virtual room planing, to robotic applications. While current volume-based view synthesis methods that use neural radiance fields (NeRFs) show promising results in reproducing the appearance of an object or scene, they do not reconstruct an actual surface. The volumetric representation… 
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43 Citations
Highly Influential Citations
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43 Citations

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What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives

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SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations

This paper addresses the problems of achieving large-scale 3D reconstructions with implicit representations using 3D LiDAR measurements, and designs an incremental mapping system with regularization to tackle the issue of catastrophic forgetting in continual learning.

BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion

This work proposes a novel bi-level fusion strategy that considers both efficiency and reconstruction quality by design, and evaluates the proposed method on multiple datasets quantitatively and qualitatively, demonstrating a significant improvement over existing methods.

ActiveRMAP: Radiance Field for Active Mapping And Planning

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Instant Volumetric Head Avatars

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