Exploring distance-aware weighting strategies for accurate reconstruction of voxel-based 3D synthetic models

In this paper, we propose and evaluate various distance-aware weighting strategies to improve reconstruction accuracy of a voxel-based model according to the Truncated Signed Distance Function (TSDF), from the data obtained by low-cost depth sensors. We look at two strategy directions: (a) weight definition strategies prioritizing importance of the sensed data depending on the data accuracy, and (b) model updating strategies defining the level of influence of the new data on the existing 3D model. In particular, we introduce Distance-Aware (DA) and Distance-Aware Slow-Saturation (DASS) updating methods to intelligently integrate the depth data into the synthetic 3D model based on the distance-sensitivity metric of a low-cost depth sensor. By quantitative and qualitative comparison of the resulting synthetic 3D models to the corresponding ground-truth models, we identify the most promising strategies, which lead to an accuracy improvement involving a reduction of the model error by 10¿-¿35%