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

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 neighbor-hood, 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.

Authors

• Jiaxin Wei 0001
• Lan Hu
• Chenyu Wang
• Laurent Kneip

Keywords

• Measurement
• Geometry
• Solid modeling
• Three-dimensional displays
• Databases
• Shape
• Pipelines
• Accuracy Of Model
• Large-scale Database
• Retrieval Accuracy
• CAD Model
• Representation Learning
• Object Shape
• Shape Descriptors
• Neural Network
• Object Detection
• Global Features
• Finite Set
• Point Cloud
• Geometric Structure
• 3D Scanning
• 3D Shape
• Distance Metrics
• Small Pool
• Nearest Neighbor Search
• Global Descriptors
• Chamfer Distance
• Earth Mover’s Distance
• Point Cloud Generation