What Makes a Good Pre-Training on Points Cloud?

[hansen7]

General Interpretability:

• interpretable ml book, specifically sections on learned features, Shapley values, Influential Instance
• "Network dissection: Quantifying interpretability of deep visual representations", CVPR 2017
• "Feature Visualisation", Olah, et al., Distill 2017.
• Bolei's Portfolio
• Chiyuan's Portfolio (also, transfer learning)

General Pre-Training:

• "Rethinking ImageNet Pre-training", ICCV 2019
• "Rethinking Pre-training and Self-training", NeurIPS 2020
• "What is being transferred in transfer learning?", NeurIPS 2020
• "What Makes Instance Discrimination Good for Transfer Learning?", ICLR 2021 Sub

Ideas from Contrastive Learning:

• Yonglong's Portfolio

Point Cloud Specific:

• "Rotation Invariant Convolutions for 3D Point Clouds Deep Learning", 3DV 2019
• "Quaternion Equivariant Capsule Networks for 3D Point Clouds", ECCV 2020
• "Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions", ECCV 2020
• "On the Universality of Rotation Equivariant Point Cloud Networks", ICLR 2021 Sub

Extensions:

• "Neural Similarity Learning", NeurIPS 2019

[GengxinLiu]

Hello, here are some other point cloud self-supervised papers

• Info3D Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning (2020 ECCV)
• Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds (2020 CVPR)