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.

Papers:

- “Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation” by Anthony Simeonov et al.
- “Vector Neurons: A General Framework for SO(3)-Equivariant Networks” by Congyue Deng et al.

Datasets mentioned:

- Shapenet: Taxonomy Viewer
- ModelNet40: https://3dshapenets.cs.princeton.edu/