BayesFusion-SDF: Probabilistic Signed Distance Fusion with View Planning on CPU
This work addresses the need for transparent uncertainty representation and efficient CPU-based reconstruction in robotics, augmented reality, and digital inspection, offering a probabilistic alternative to GPU-heavy neural methods.
The paper tackles the problem of dense 3D reconstruction from depth observations by introducing BayesFusion-SDF, a probabilistic signed distance fusion framework that runs on CPU. It achieves higher geometric accuracy than traditional TSDF baselines and provides uncertainty estimates for active sensing, as demonstrated on controlled ablation scenes and CO3D object sequences.
Key part of robotics, augmented reality, and digital inspection is dense 3D reconstruction from depth observations. Traditional volumetric fusion techniques, including truncated signed distance functions (TSDF), enable efficient and deterministic geometry reconstruction; however, they depend on heuristic weighting and fail to transparently convey uncertainty in a systematic way. Recent neural implicit methods, on the other hand, get very high fidelity but usually need a lot of GPU power for optimization and aren't very easy to understand for making decisions later on. This work presents BayesFusion-SDF, a CPU-centric probabilistic signed distance fusion framework that conceptualizes geometry as a sparse Gaussian random field with a defined posterior distribution over voxel distances. First, a rough TSDF reconstruction is used to create an adaptive narrow-band domain. Then, depth observations are combined using a heteroscedastic Bayesian formulation that is solved using sparse linear algebra and preconditioned conjugate gradients. Randomized diagonal estimators are a quick way to get an idea of posterior uncertainty. This makes it possible to extract surfaces and plan the next best view while taking into account uncertainty. Tests on a controlled ablation scene and a CO3D object sequence show that the new method is more accurate geometrically than TSDF baselines and gives useful estimates of uncertainty for active sensing. The proposed formulation provides a clear and easy-to-use alternative to GPU-heavy neural reconstruction methods while still being able to be understood in a probabilistic way and acting in a predictable way. GitHub: https://mazumdarsoumya.github.io/BayesFusionSDF