A Bayesian Approach for Task-Specific Next-Best-View Selection with Uncertain Geometry
We develop a framework for task-specific active next-best-view selection in 3D reconstruction from point clouds, by casting the problem in the language of Bayesian decision theory. Our framework works by (a) placing a prior distribution over the space of implicit surfaces, (b) using recently-developed stochastic surface reconstruction methods to calculate the resulting posterior distribution, then (c) using the posterior distribution to carefully reason about which view to scan next. This enables us to perform camera selection in a manner that is directly optimized for the intended use of the reconstructed data - meaning, we reduce uncertainty only in those regions that make a difference ...
| Authors | Jingsen Zhu, Silvia Sellán, Alexander Terenin |
|---|---|
| Primary category | cs.GR |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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