Compositionally Generalizable 3D Structure Prediction

Understand

Single-image 3D shape reconstruction is an important and long-standing problem in computer vision.

• A plethora of existing works is constantly pushing the state-of-the-art performance in the deep learning era.
• However, there remains a much more difficult and under-explored issue on how to generalize the learned skills over unseen object categories that have very different shape geometry distributions.
• In this paper, we bring in the concept of compositional generalizability and propose a novel framework that could better generalize to these unseen categories.