Upright-Net+: Enhanced Learning of Upright Orientation for 3D Point Clouds

Area of Science:

• Computer Vision
• Machine Learning
• 3D Shape Analysis

Background:

• 3D shape analysis is challenged by pose variations in deep learning.
• Estimating upright orientation is crucial for 3D model interpretation.

Purpose of the Study:

• To present Upright-Net+, an enhanced model for accurate upright orientation estimation in 3D point clouds.
• To address pose complexities in deep learning frameworks for 3D analysis.

Main Methods:

• Reformulated continuous orientation problem into a discrete classification task.
• Introduced Global Positional Encoding Module using Relative Distance Histogram Statistics Embedding (GPE-RDHS).
• Enhanced weighted residual loss to penalize false positives.

Main Results:

• Upright-Net+ demonstrates exceptional performance in upright orientation estimation.
• Learned orientation-aware features significantly benefit downstream tasks, especially classification.

Conclusions:

• The "form follows function" principle guides the identification of object bases for orientation.
• Upright-Net+ effectively mitigates over-smoothing and enhances feature learning for 3D analysis.