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

Xufang Pang, Feng Li, Hongjie Zhuang

IEEE Transactions on Visualization and Computer Graphics

|September 2, 2025

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Upright-Net+ estimates the upright orientation of 3D point clouds by identifying an object's natural base. This method improves 3D shape analysis and downstream classification tasks.

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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.