A Cross-Domain Benchmark of Intrinsic and Post Hoc Explainability for 3D Deep Learning Models

Deep learning models for three-dimensional (3D) data are increasingly used in domains such as medical imaging, object recognition, and robotics. At the same time, the use of AI in these domains is increasing, while, due to their black-box nature, the need for explainability has grown significantly. However, the lack of standardized and quantitative benchmarks for explainable artificial intelligence (XAI) in 3D data limits the reliable comparison of explanation quality. In this paper, we present a unified benchmarking framework to evaluate both intrinsic and post hoc XAI methods across three representative 3D datasets: volumetric CT scans (MosMed), voxelized CAD models (ModelNet40), and real-world point clouds (ScanObjectNN). The evaluated methods include Grad-CAM, Integrated Gradients, Saliency, Occlusion, and the intrinsic ResAttNet-3D model. We quantitatively assess explanations using the Correctness (AOPC), Completeness (AUPC), and Compactness metrics, consistently applied across all datasets. Our results show that explanation quality significantly varies across methods and domains, demonstrating that Grad-CAM and intrinsic attention performed best on medical CT scans, while gradient-based methods excelled on voxelized and point-based data. Statistical tests (Kruskal-Wallis and Mann-Whitney U) confirmed significant performance differences between methods. No single approach achieved superior results across all domains, highlighting the importance of multi-metric evaluation. This work provides a reproducible framework for standardized assessment of 3D explainability and comparative insights to guide future XAI method selection.