HypCAD: Geometry-Enhanced Hyperbolic Contrastive Learning for CAD Model Retrieval

Abstract

Retrieving CAD models for real-world object scans enhances object-level mapping, providing a nuanced spatial understanding crucial for precise interactions in robotics or mixed reality. Commonly, CAD model retrieval is performed by matching features learned in Euclidean space. However, learning discriminative features in Euclidean space faces significant challenges, primarily due to its flat nature and the wide variety of CAD models with different levels of detail. To address the limitations of Euclidean space and improve CAD model retrieval, this paper introduces HypCAD, a contrastive learning framework in hyperbolic space. We present a novel geometry-enhanced hyperbolic distance and utilize a three-component contrastive learning loss to learn hyperbolic feature representations for the CAD model retrieval task. We demonstrate HypCAD’s superior retrieval accuracy through comparisons with baseline contrastive learning methods on both the synthetic ShapeNet dataset and the real-world Scan2CAD dataset.