Accurate Instance-Level CAD Model Retrieval in a Large-Scale Database

TL;DR

This paper introduces a geometry-based re-ranking method for instance-level CAD model retrieval in large-scale databases, significantly improving accuracy over existing approaches by leveraging neighborhood information and a novel distance metric.

Contribution

It proposes a novel re-ranking approach that combines learned representations with a robust point set distance to enhance fine-grained CAD model retrieval accuracy.

Findings

Significant improvement in retrieval accuracy over state-of-the-art methods.

Effective neighborhood-based re-ranking enhances model discrimination.

Robust point set distance metric improves fine-grained retrieval results.

Abstract

We present a new solution to the fine-grained retrieval of clean CAD models from a large-scale database in order to recover detailed object shape geometries for RGBD scans. Unlike previous work simply indexing into a moderately small database using an object shape descriptor and accepting the top retrieval result, we argue that in the case of a large-scale database a more accurate model may be found within a neighborhood of the descriptor. More importantly, we propose that the distinctiveness deficiency of shape descriptors at the instance level can be compensated by a geometry-based re-ranking of its neighborhood. Our approach first leverages the discriminative power of learned representations to distinguish between different categories of models and then uses a novel robust point set distance metric to re-rank the CAD neighborhood, enabling fine-grained retrieval in a large shape database. Evaluation on a real-world dataset shows that our geometry-based re-ranking is a conceptually simple but highly effective method that can lead to a significant improvement in retrieval accuracy compared to the state-of-the-art.