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.

翻译：多分辨率体素网格上的插值特征直接优化已成为MLP类模块的更高效替代方案。然而，该方法受限于更高的内存开销和有限的表示能力。本文提出一种新型动态网格优化方法，用于融合RGB与深度观测的高保真三维表面重建。不同于对每个体素平等待，我们通过动态调整网格，将更精细尺度体素分配给复杂度更高的区域，从而捕捉更丰富的细节。此外，我们设计了一种无需任何先验知识即可在优化过程中量化体素网格动态细化的方案。该方法能在合成数据与真实数据上生成具有精细细节的高质量三维重建，同时保持计算效率——其速度显著优于基线方法NeuralRGBD。