Learning 3D Part Detection from Sparsely Labeled Data

For large collections of 3D models, the ability to detect and localize parts of interest is necessary to provide search and visualization enhancements beyond simple high-level categorization. While current 3D labeling approaches rely on learning from fully labeled meshes, such training data is difficult to acquire at scale. In this work we explore learning to detect object parts from sparsely labeled data, i.e. we operate under the assumption that for any object part we have only one labeled vertex rather than a full region segmentation. Similarly, we also learn to output a single representative vertex for each detected part. Such localized predictions are useful for applications where visualization is important. Our approach relies heavily on exploiting the spatial configuration of parts on a model to drive the detection. Inspired by structured multi-class object detection models for images, we develop an algorithm that combines independently trained part classifiers with a structured SVM model, and show promising results on real-world textured 3D data.