DePoint: Improving rotation robustness of 3D point cloud analysis via decreasing entropy.

Area of Science:

• Computer Vision
• Machine Learning
• 3D Data Analysis

Background:

• Rotation robustness is critical for 3D point cloud analysis due to unpredictable object orientations.
• Current methods often struggle with precise alignment in the vast rotation space.
• Random rotations increase joint entropy between point clouds and semantic labels, degrading model performance.

Purpose of the Study:

• To investigate the impact of rotation on point cloud analysis.
• To introduce DePoint, a novel method to enhance rotation robustness.
• To decrease entropy by aligning spatial distributions with semantic information.

Main Methods:

• Investigated the impact of rotation on point cloud entropy.
• Introduced DePoint, a rotation enhancement method.
• Utilized a Siamese point cloud encoder with a shared task head and an auxiliary classifier.

Main Results:

• DePoint effectively decreases entropy in rotated point cloud representations.
• The method ensures semantic consistency in learned representations.
• Seamless integration into existing models without inference-time parameters.

Conclusions:

• DePoint significantly improves rotation robustness in point cloud models.
• The method enhances performance in 3D object classification and segmentation.
• DePoint offers a simple yet effective solution for real-world 3D analysis challenges.