Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey (2202.13589v3)

Abstract: Point cloud data have been widely explored due to its superior accuracy and robustness under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved very impressive success in various applications such as surveillance and autonomous driving. The convergence of point cloud and DNNs has led to many deep point cloud models, largely trained under the supervision of large-scale and densely-labelled point cloud data. Unsupervised point cloud representation learning, which aims to learn general and useful point cloud representations from unlabelled point cloud data, has recently attracted increasing attention due to the constraint in large-scale point cloud labelling. This paper provides a comprehensive review of unsupervised point cloud representation learning using DNNs. It first describes the motivation, general pipelines as well as terminologies of the recent studies. Relevant background including widely adopted point cloud datasets and DNN architectures is then briefly presented. This is followed by an extensive discussion of existing unsupervised point cloud representation learning methods according to their technical approaches. We also quantitatively benchmark and discuss the reviewed methods over multiple widely adopted point cloud datasets. Finally, we share our humble opinion about several challenges and problems that could be pursued in future research in unsupervised point cloud representation learning. A project associated with this survey has been built at https://github.com/xiaoaoran/3d_url_survey.

• The paper presents a comprehensive taxonomy of unsupervised techniques, detailing generation-, context-, and local descriptor-based methods.
• It demonstrates that unsupervised approaches can narrow the performance gap with supervised models, as shown through benchmark tests on datasets like ModelNet40 and ShapeNet.
• It highlights key challenges and future directions, including scalability, multimodal integration, and the need for specialized evaluation metrics in 3D data processing.

Unsupervised Point Cloud Representation Learning with Deep Neural Networks: An Overview

The paper by Xiao et al. presents a thorough survey on the burgeoning research area of unsupervised point cloud representation learning using deep neural networks (DNNs). With the increasing adoption of 3D data across diverse fields such as autonomous driving, robotics, and medical imaging, the effective processing and understanding of 3D point clouds have become highly relevant. This paper reviews existing approaches that leverage unsupervised techniques to learn useful representations from point cloud data, a task traditionally reliant on large volumes of labeled data and hence, often constrained by the availability of annotations.

Taxonomy of Unsupervised Methods

The authors provide a well-structured taxonomy of unsupervised learning methods for point clouds, categorizing them based on the primary pretext tasks used for representation learning. The primary categories include:

1. Generation-based methods: These approaches learn representations by reconstructing the input data. They include autoencoder-based methods such as FoldingNet and GAN-based approaches like 3D-GAN. These methods focus on various tasks such as point cloud completion, up-sampling, and self-reconstruction to learn robust representations.
2. Context-based methods: These approaches exploit intrinsic spatial, temporal, or contextual relationships within the data. Techniques like point cloud contrastive learning (PointContrast) fall under this category, leveraging view-invariance and spatial reasoning to enhance representation learning.
3. Multiple modal-based methods: Here, learning is augmented by utilizing additional modalities. Such approaches derive their strength from leveraging multi-modal correspondences, bringing additional semantic richness to the learned representations.
4. Local descriptor-based methods: These focus on learning fine-grained, localized features that can capture intricate details necessary for tasks like point matching or registration.

Performance Evaluation and Challenges

The evaluation of these unsupervised learning methods over benchmark datasets such as ModelNet40, ShapeNet, and real-world datasets like S3DIS and ScanNet-V2 highlights the gradual closing of the performance gap between unsupervised and supervised methods. While recent methods like Point-BERT have demonstrated competitive results, the scalability and adaptability of these models across varied tasks and datasets remain points of ongoing research.

The authors emphasize several challenges facing the field, such as the need for larger and more diverse datasets, especially for scene-level tasks. The research community is encouraged to standardize point cloud processing backbones akin to those in 2D vision, which could accelerate advancements in this domain.

Implications and Future Directions

The paper outlines key implications of advancing unsupervised learning techniques for point clouds. These include reducing dependency on labeled data, improving the generalization of models across different domains, and facilitating the design of more adaptable AI systems capable of operating in dynamic and multifaceted environments.

Future work in this area could significantly benefit from exploring more robust learning paradigms that integrate multiple modalities or leverage spatio-temporal information in more sophisticated ways. Additionally, developing evaluation metrics specifically suited for assessing unsupervised representations in 3D would offer deeper insights into these networks' capabilities.

In summary, Xiao et al.'s survey serves as an important resource for deepening our understanding of unsupervised point cloud representation learning, laying down a comprehensive foundation that opens up numerous avenues for future exploration and development in AI. The paper's exploration of current techniques, benchmarks, and future prospects marks a significant step forward in the ongoing evolution of 3D data processing technologies.