NeuroSURF: Neural Uncertainty-aware Robust Surface Reconstruction

Neural implicit functions have become popular for representing surfaces because they offer an adaptive resolution and support arbitrary topologies. While previous works rely on ground truth point clouds, they often ignore the effect of input quality and sampling methods on the reconstruction. In this paper, we introduce NeuroSURF, which generates significantly improved qualitative and quantitative reconstructions driven by a novel sampling and interpolation technique. We show that employing a sampling technique that considers the geometric characteristics of inputs can enhance the training process. To this end, we introduce a strategy that efficiently computes differentiable geometric features, namely, mean curvatures, to augment the sampling phase during the training period. Moreover, we augment the neural implicit surface representation with uncertainty, which offers insights into the occupancy and reliability of the output signed distance value, thereby expanding representation capabilities into open surfaces. Finally, we demonstrate that NeuroSURF leads to state-of-the-art reconstructions on both synthetic and real-world data.