DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation

Scalable 6D pose estimation for rigid objects from RGB images aims at handling multiple objects and generalizing to novel objects. Building on a well-known auto-encoding framework to cope with object symmetry and the lack of labeled training data, we achieve scalability by disentangling the latent representation of auto-encoder into shape and pose sub-spaces. The latent shape space models the similarity of different objects through contrastive metric learning, and the latent pose code is compared with canonical rotations for rotation retrieval. Because different object symmetries induce inconsistent latent pose spaces, we re-entangle the shape representation with canonical rotations to generate shape-dependent pose codebooks for rotation retrieval. We show state-of-the-art performance on two benchmarks containing textureless CAD objects without category and daily objects with categories respectively, and further demonstrate improved scalability by extending to a more challenging setting of daily objects across categories.

Automated summary

This paper presents a scalable 6D pose estimation method for rigid objects from RGB images. By using an auto-encoding framework and contrastive metric learning, the method can handle multiple objects and generalize to novel objects. Experimental results on two benchmarks show state-of-the-art performance, and improved scalability is demonstrated in a more challenging setting.