What question did this study set out to answer?

The aim is to develop a reliable system for detecting indoor changes using 3D LiDAR data without manual annotation.

April 17, 2026AI0 citationsOpen Access

SPICD-Net: A Siamese PointNet Approach for Indoor 3D Change Detection

SPICD-Net: A Siamese PointNet Framework for Autonomous Indoor Change Detection in 3D LiDAR Point Clouds

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This framework detects indoor changes in 3D environments, suggesting lightweight strategies for autonomous robots.

The aim is to develop a reliable system for detecting indoor changes using 3D LiDAR data without manual annotation.
Developed a Siamese PointNet framework for classifying tile pairs into no-change, changed, and inconsistent categories.
Implemented a synthetic anomaly injection strategy to align training with real-time processing needs.
Introduced a stochastic-gated Chamfer-statistics branch to enhance geometric feature analysis under hardware constraints.
Achieved Precision = 0.86, Recall = 0.82, F1-score = 0.84, and Accuracy = 0.96 on 14 simulation experiments.
No false positives were recorded in the no-change baseline, and mean inference time was 22.4 seconds per 172-tile map.
Limited real-world tests resulted in Precision = 0.583, Recall = 1.000, and F1 = 0.737 for an unseen room.

Šeljmeši et al. (2026) studied this question.

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April 17, 2026AI0 citationsOpen Access

SPICD-Net: A Siamese PointNet Framework for Autonomous Indoor Change Detection in 3D LiDAR Point Clouds

View Full Paper

Ask AI

Bookmark

Authors

This framework detects indoor changes in 3D environments, suggesting lightweight strategies for autonomous robots.

The aim is to develop a reliable system for detecting indoor changes using 3D LiDAR data without manual annotation.
Developed a Siamese PointNet framework for classifying tile pairs into no-change, changed, and inconsistent categories.
Implemented a synthetic anomaly injection strategy to align training with real-time processing needs.
Introduced a stochastic-gated Chamfer-statistics branch to enhance geometric feature analysis under hardware constraints.
Achieved Precision = 0.86, Recall = 0.82, F1-score = 0.84, and Accuracy = 0.96 on 14 simulation experiments.
No false positives were recorded in the no-change baseline, and mean inference time was 22.4 seconds per 172-tile map.
Limited real-world tests resulted in Precision = 0.583, Recall = 1.000, and F1 = 0.737 for an unseen room.

Šeljmeši et al. (2026) studied this question.

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