A 3D Reconstruction Benchmark for Asset Inspection

TL;DR

This paper introduces a new 3D reconstruction dataset tailored for asset inspection, highlighting the challenges current methods face with complex, high-detail, and reflective surfaces in synthetic scenes, and emphasizing the need for improved algorithms.

Contribution

The paper provides a novel dataset with ground truth for 3D reconstruction in asset inspection scenarios, enabling benchmarking and revealing limitations of existing methods.

Findings

Current reconstruction methods perform poorly on dense, high-detail scenes.

Existing approaches struggle with non-Lambertian surfaces and complex geometries.

The dataset exposes a significant scalability gap in current 3D reconstruction techniques.

Abstract

Asset management requires accurate 3D models to inform the maintenance, repair, and assessment of buildings, maritime vessels, and other key structures as they age. These downstream applications rely on high-fidelity models produced from aerial surveys in close proximity to the asset, enabling operators to locate and characterise deterioration or damage and plan repairs. Captured images typically have high overlap between adjacent camera poses, sufficient detail at millimetre scale, and challenging visual appearances such as reflections and transparency. However, existing 3D reconstruction datasets lack examples of these conditions, making it difficult to benchmark methods for this task. We present a new dataset with ground truth depth maps, camera poses, and mesh models of three synthetic scenes with simulated inspection trajectories and varying levels of surface condition on non-Lambertian scene content. We evaluate state-of-the-art reconstruction methods on this dataset. Our results demonstrate that current approaches struggle significantly with the dense capture trajectories and complex surface conditions inherent to this domain, exposing a critical scalability gap and pointing toward new research directions for deployable 3D reconstruction in asset inspection. Project page: https://roboticimaging.org/Projects/asset-inspection-dataset/