LoDA: A Level of Detection Aware Method and a Multimodal Sensing Benchmark for Object Level Change Detection

2026-08-05

AI Summary

This study addresses the challenge of reliable object-level 3D change detection in multi-temporal urban LiDAR data, where existing methods suffer from ambiguous detection limits and inconsistent labeling. To overcome these limitations, this work proposes the first object-oriented 3D change detection framework that explicitly incorporates the Level of Detection (LoD) concept. By decoupling registration, geometric, and semantic modules, the approach propagates pose uncertainty into spatial change LoDs, thereby enhancing cross-temporal correspondence stability and suppressing false positives. The method integrates multi-modal sensing (LiDAR/GNSS/IMU), geometry-driven object proxies, regularized semantic and instance segmentation, and multi-dimensional features—including height, volume, and normal displacement—to produce five confidence-aware change labels. Evaluated on the newly introduced LoDA benchmark, it achieves 95.0% accuracy, 90.8% macro F1, and 83.0% macro IoU, and attains 96.81% average accuracy and 89.52% average change IoU on Urb3DCD-V2, significantly outperforming state-of-the-art approaches.

Abstract

High-definition 3D LiDAR maps are important for autonomous driving and smart-city services, which require reliable detection of object-level changes in multi-temporal urban LiDAR to keep digital maps aligned with the physical world. Existing approaches from raster height differencing to depth image and point-cloud networks often remain tile-based and threshold-driven, yielding per-point scores without explicit detection limits or consistent object-level labels. We propose an object-level 3D change-detection pipeline that integrates detection-limit-aware registration, geometry-driven object proxies with rule-based semantic and instance segmentation, and displacement cues in height, volume, and surface-normal direction to assign five change labels with confidence. By decoupling registration, geometry, and semantics, the pipeline propagates pose uncertainty into spatially varying detection limits, stabilizes cross-epoch correspondences, and suppresses false changes caused by residual misalignment and density variation. We also present LoDA, a level-of-detection (LoD) aware benchmark for the Subiaco district with fused multi-temporal vehicle-LiDAR maps constructed with LiDAR, GNSS, and IMU support, semantic instances, and object-level annotations. On this benchmark, our method achieves 95.0% accuracy, 90.8% macro F1, and 83.0% macro IoU, exceeding the best baseline by 8.7 IoU points and 4.4 F1 points. On the public Urb3DCD-V2 benchmark evaluated under the official point-wise protocol, it reaches 96.81% mean accuracy and 89.52% mean change IoU, improving over the strongest reported baselines by 1.36 points in mAcc and 3.18 points in mIoUch.

Problem

• object-level change detection
• 3D LiDAR maps
• detection limits
• multi-temporal urban LiDAR
• digital map alignment

Innovation

• Level of Detection
• Object-level Change Detection
• 3D LiDAR
• Multimodal Benchmark
• Detection-limit-aware Registration