Wang, J;
Bleja, T;
Agapito, L;
(2023)
GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface Reconstruction.
In:
Proceedings - 2022 International Conference on 3D Vision, 3DV 2022.
(pp. pp. 433-442).
IEEE: Prague, Czechia.
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Abstract
We present GO-Surf, a direct feature grid optimization method for accurate and fast surface reconstruction from RGB-D sequences. We model the underlying scene with a learned hierarchical feature voxel grid that encapsulates multi-level geometric and appearance local information. Feature vectors are directly optimized such that after being tri-linearly interpolated, decoded by two shallow MLPs into signed distance and radiance values, and rendered via volume rendering, the discrepancy between synthesized and observed RGB/depth values is minimized. Our supervision signals - RGB, depth and approximate SDF - can be obtained directly from input images without any need for fusion or post-processing. We formulate a novel SDF gradient regularization term that encourages surface smoothness and hole filling while maintaining high frequency details. GO-Surf can optimize sequences of 1-2K frames in 15-45 minutes, a speedup of \times 60 over NeuralRGB-D [1], the most related approach based on an MLP representation, while maintaining on par performance on standard benchmarks. Project page: https://jingwenwang95.github.io/go_surf.
| Type: | Proceedings paper |
|---|---|
| Title: | GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface Reconstruction |
| Event: | 2022 International Conference on 3D Vision (3DV) |
| Dates: | 12 Sep 2022 - 16 Sep 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/3DV57658.2022.00055 |
| Publisher version: | https://doi.org/10.1109/3DV57658.2022.00055 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Training , Surface reconstruction , Three-dimensional displays , Optimization methods , Benchmark testing , Rendering (computer graphics) , Filling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10168677 |
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