VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for
  Enhanced Indoor View Synthesis
Sen Wang Wei Zhang Stefano Gasperini Shun-Cheng Wu Nassir Navab
Creating high-quality view synthesis is essential for immersive applications
but continues to be problematic, particularly in indoor environments and for
real-time deployment. Current techniques frequently require extensive
computational time for both training and rendering, and often produce
less-than-ideal 3D representations due to inadequate geometric structuring. To
overcome this, we introduce VoxNeRF, a novel approach that leverages volumetric
representations to enhance the quality and efficiency of indoor view synthesis.
Firstly, VoxNeRF constructs a structured scene geometry and converts it into a
voxel-based representation. We employ multi-resolution hash grids to adaptively
capture spatial features, effectively managing occlusions and the intricate
geometry of indoor scenes. Secondly, we propose a unique voxel-guided efficient
sampling technique. This innovation selectively focuses computational resources
on the most relevant portions of ray segments, substantially reducing
optimization time. We validate our approach against three public indoor
datasets and demonstrate that VoxNeRF outperforms state-of-the-art methods.
Remarkably, it achieves these gains while reducing both training and rendering
times, surpassing even Instant-NGP in speed and bringing the technology closer
to real-time.