BayesFusion-SDF: Probabilistic Signed Distance Fusion with View Planning on CPU
Soumya Mazumdar, Vineet Kumar Rakesh, Tapas Samanta

TL;DR
BayesFusion-SDF introduces a CPU-based probabilistic 3D reconstruction method that models geometry as a Gaussian random field, providing accurate results and uncertainty estimates for view planning, offering an interpretable alternative to neural methods.
Contribution
It presents a novel probabilistic SDF fusion framework that uses Bayesian formulation and sparse linear algebra, enabling uncertainty-aware 3D reconstruction on CPU.
Findings
More accurate than TSDF baselines in geometry reconstruction
Provides useful uncertainty estimates for active sensing
Operates efficiently on CPU without GPU dependence
Abstract
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…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Optical Sensing Technologies · Advanced Vision and Imaging
