Papers on Equivariant Neural Networks for Object Representation - 11 February, 2022

Heiko Hoffmann gives an overview of the “Neural Descriptor Fields” paper. He first goes over how the Neural Descriptor Fields (NDFs) function represents key points on a 3D object relative to its position and pose, and how NDFs can be used to recover an object’s position and pose. He then discusses the paper’s simulation and robot-experiment results and highlights the useful concepts and limits of the paper. In the second half of the meeting, Karan Grewal presents the “Vector Neurons” paper. He first gives a quick review of the core concepts and terminology of the paper. Then he looks into the structure of the paper’s SO(3)-equivariant neural networks in detail and how the networks represent object pose and rotation. Lastly, Karan goes over the results of object classification and image reconstruction and points out a few shortcomings. “Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation” by Anthony Simeonov et al.: “Vector Neurons: A General