A Bayesian Approach for Task-Specific Next-Best-View Selection with Uncertain Geometry
Jingsen Zhu, Silvia Sellán, Alexander Terenin
TLDR
A Bayesian framework optimizes next-best-view selection for 3D reconstruction, reducing task-specific uncertainty more efficiently than prior methods.
Key contributions
- Introduces a Bayesian decision theory framework for active next-best-view selection in 3D reconstruction.
- Optimizes camera selection by reducing uncertainty only in task-relevant regions, unlike uniform approaches.
- Leverages prior and posterior distributions over implicit surfaces for informed view planning.
- Achieves superior performance with fewer views across semantic classification, segmentation, and physics simulation.
Why it matters
This paper significantly improves 3D reconstruction efficiency by focusing on task-specific uncertainty reduction. It enables more accurate and resource-efficient data acquisition for downstream applications like classification and simulation. This is crucial for practical robotic and computer vision systems.
Original Abstract
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 in the task at hand, as opposed to prior approaches that reduce it uniformly across space. We evaluate our method across three distinct downstream tasks: semantic classification, segmentation, and PDE-guided physics simulation. Experimental results demonstrate that our framework achieves superior task performance with fewer views compared to commonly used baselines and prior general uncertainty-reduction techniques.
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