Proactive Estimation of Occlusions and Scene Coverage for Planning Next Best Views in an Unstructured Representation

TL;DR

This paper introduces proactive methods for estimating occlusions and scene coverage in unstructured representations to optimize Next Best View planning, reducing views and travel distance while maintaining high observation quality.

Contribution

It proposes novel proactive techniques for occlusion and coverage estimation in unstructured scene representations, enhancing view planning efficiency.

Findings

Fewer views are needed for scene observation.

Reduced travel distance in view planning.

Maintains high observation quality with lower computational cost.

Abstract

The process of planning views to observe a scene is known as the Next Best View (NBV) problem. Approaches often aim to obtain high-quality scene observations while reducing the number of views, travel distance and computational cost. Considering occlusions and scene coverage can significantly reduce the number of views and travel distance required to obtain an observation. Structured representations (e.g., a voxel grid or surface mesh) typically use raycasting to evaluate the visibility of represented structures but this is often computationally expensive. Unstructured representations (e.g., point density) avoid the computational overhead of maintaining and raycasting a structure imposed on the scene but as a result do not proactively predict the success of future measurements. This paper presents proactive solutions for handling occlusions and considering scene coverage with an unstructured representation. Their performance is evaluated by extending the density-based Surface Edge Explorer (SEE). Experiments show that these techniques allow an unstructured representation to observe scenes with fewer views and shorter distances while retaining high observation quality and low computational cost.