  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.
