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

• URL: http://arxiv.org/abs/2304.06178v1
• Date: Wed, 12 Apr 2023 22:39:57 GMT
• Title: Dynamic Voxel Grid Optimization for High-Fidelity RGB-D Supervised Surface Reconstruction
• Authors: Xiangyu Xu, Lichang Chen, Changjiang Cai, Huangying Zhan, Qingan Yan, Pan Ji, Junsong Yuan, Heng Huang, Yi Xu
• Abstract summary: We introduce a novel dynamic grid optimization method for high-fidelity 3D surface reconstruction. We optimize the process by dynamically modifying the grid and assigning more finer-scale voxels to regions with higher complexity. The proposed approach is able to generate high-quality 3D reconstructions with fine details on both synthetic and real-world data.
• Score: 130.84162691963536
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Direct optimization of interpolated features on multi-resolution voxel grids has emerged as a more efficient alternative to MLP-like modules. However, this approach is constrained by higher memory expenses and limited representation capabilities. In this paper, we introduce a novel dynamic grid optimization method for high-fidelity 3D surface reconstruction that incorporates both RGB and depth observations. Rather than treating each voxel equally, we optimize the process by dynamically modifying the grid and assigning more finer-scale voxels to regions with higher complexity, allowing us to capture more intricate details. Furthermore, we develop a scheme to quantify the dynamic subdivision of voxel grid during optimization without requiring any priors. The proposed approach is able to generate high-quality 3D reconstructions with fine details on both synthetic and real-world data, while maintaining computational efficiency, which is substantially faster than the baseline method NeuralRGBD.

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