Two grids are better than one: Hybrid indoor scene reconstruction framework with adaptive priors

Indoor scene reconstruction from multi-view images is a pivotal technology within the field of robotics and augmented reality. Previous researches have predominantly focused on neural radiance fields aided by geometric monocular priors. However, due to the inductive smoothness bias introduced by deep Multi-Layer Perceptron (MLP) networks, these methods struggle to recover the scene surface with complex and fine geometry details. Additionally, when used as additional supervision signals during optimization, priors in different regions make different contributions. Simply incorporating them in all regions may lead to a decrease in the accuracy. To tackle these issues, we present a generic end-to-end framework named AdaptSurf, which combines Signed Distance Field (SDF) voxel grids and feature voxel grids to enhance the capability of reconstructing accurate geometry details, respectively. Furthermore, we design a policy network to adaptively enable the estimated depth or normal priors to supervise the learning process, which improves the reconstruction accuracy and accelerates neural surface reconstruction. Qualitative and quantitative experiments show that AdaptSurf yields high-quality surfaces, especially for fine-grained details and smooth regions. Furthermore, the policy network exhibits an interpretable behavior that depends on the voxel features, which helps to improve the quality of surface reconstruction.