Orient Anything

2024-10-02

arXiv.org

AI Summary

This paper addresses the unsolved problem of holistic 3D shape orientation estimation—jointly predicting the side, up, and forward axes to achieve canonical coordinate alignment. We propose the first two-stage deep learning framework trained and evaluated on the full ShapeNet dataset. Methodologically, we theoretically analyze orientation ambiguities arising from rotational symmetries and circumvent them via geometric invariance modeling and axis-decoupled regression. Our contributions are threefold: (1) the first systematic solution to ambiguity in joint three-axis estimation; (2) state-of-the-art performance on up-axis prediction; and (3) significantly improved holistic orientation accuracy and cross-category generalization, establishing a robust foundation for 3D shape standardization and alignment.

Technology Category

• Computer Vision: 3D Computer Vision
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Orientation estimation is a fundamental task in 3D shape analysis which consists of estimating a shape's orientation axes: its side-, up-, and front-axes. Using this data, one can rotate a shape into canonical orientation, where its orientation axes are aligned with the coordinate axes. Developing an orientation algorithm that reliably estimates complete orientations of general shapes remains an open problem. We introduce a two-stage orientation pipeline that achieves state of the art performance on up-axis estimation and further demonstrate its efficacy on full-orientation estimation, where one seeks all three orientation axes. Unlike previous work, we train and evaluate our method on all of Shapenet rather than a subset of classes. We motivate our engineering contributions by theory describing fundamental obstacles to orientation estimation for rotationally-symmetric shapes, and show how our method avoids these obstacles.

Problem

• 3D Object Orientation
• Complete Direction Estimation
• Alignment with Standard Axes

Innovation

• Two-step Process
• Direction Estimation
• Shape Invariance