新数据集挑战3D重建在复杂表面和密集拍摄下的表现
A 3D Reconstruction Benchmark for Asset Inspection
- 构建含真实检测轨迹与非朗伯表面的合成3D场景数据集
- 现有方法在高重叠拍摄与复杂材质下精度显著下降
- 适合关注工业级3D重建的科研与工程人员
资产维护需要精确的3D模型来支持建筑、船舶等关键结构的老化评估与维修规划。这些应用依赖于近距离航拍获取的高保真模型，以定位和识别损伤并制定修复方案。采集图像通常具有高重叠度、毫米级细节，且存在反光、透明等复杂视觉特性。然而现有3D重建数据集缺乏此类条件，难以有效评估相关方法。本文提出一个新数据集，包含三个合成场景的真值深度图、相机位姿及网格模型，模拟了不同表面状态的非朗伯材质与仿真检测轨迹。我们在该数据集上评估了主流重建方法，结果表明当前技术在密集拍摄轨迹和复杂表面条件下表现严重退化，暴露出显著的可扩展性差距，指明了可部署3D重建在资产检测中的新研究方向。
原文摘要 · Abstract (English)
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/
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