Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds (2109.00179v1)

Abstract: To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by camera views, lighting, occlusions, etc. In this paper, we tackle this challenge by introducing a spatio-temporal representation learning (STRL) framework, capable of learning from unlabeled 3D point clouds in a self-supervised fashion. Inspired by how infants learn from visual data in the wild, we explore the rich spatio-temporal cues derived from the 3D data. Specifically, STRL takes two temporally-correlated frames from a 3D point cloud sequence as the input, transforms it with the spatial data augmentation, and learns the invariant representation self-supervisedly. To corroborate the efficacy of STRL, we conduct extensive experiments on three types (synthetic, indoor, and outdoor) of datasets. Experimental results demonstrate that, compared with supervised learning methods, the learned self-supervised representation facilitates various models to attain comparable or even better performances while capable of generalizing pre-trained models to downstream tasks, including 3D shape classification, 3D object detection, and 3D semantic segmentation. Moreover, the spatio-temporal contextual cues embedded in 3D point clouds significantly improve the learned representations.

• The paper introduces a spatio-temporal framework that significantly improves self-supervised 3D point cloud representation.
• The paper employs a dual network design with online and target models to effectively learn invariances using spatial and temporal augmentation.
• The paper demonstrates enhanced performance in 3D classification, detection, and segmentation across synthetic and real-world datasets.

An Overview of Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds

The paper presents an advanced framework known as Spatio-temporal Representation Learning (STRL) for self-supervised learning from 3D point clouds, addressing the challenges associated with 3D scene understanding tasks. Inherent difficulties such as variations introduced by camera views, lighting, and occlusions often hinder the development of practical and generalizable pre-trained models for 3D tasks, a gap this research aims to fill.

Framework for STRL

The framework leverages the spatial and temporal context present in 3D point clouds, drawing inspiration from human-like learning by observing changes and consistencies over time. STRL employs two temporally-correlated frames from a 3D point cloud sequence. The framework applies spatial data augmentation and then learns invariant representations through a self-supervised methodology. The learning process takes notes from successful strategies in image and video self-supervised learning, incorporating data augmentation and contrastive principles to capture data invariance effectively.

The primary architecture involves two networks: an online network and a target network. The online network predicts the target network's representations through a predictor, with the target network being steadily updated by the online network's moving average. This facilitates a robust learning process using the temporal correlation between consecutive frames or augmented views of static shapes, allowing STRL to infer and capture the variabilities and invariances in spatial structures effectively.

Methodological Implementation

The research employs extensive experiments across various types of datasets: synthetic, indoor, and outdoor. Using the ShapeNet dataset, training involved synthetic augmentation by randomly applying rotations, translations, and scaling. For natural datasets such as ScanNet and KITTI, data augmentation included back-projecting frames with camera poses to produce consistent world-coordinated point clouds across sequences.

Experimental Results

The experimental evaluation shows that STRL yields competitive or superior performance compared to conventional supervised learning on several tasks, including 3D shape classification, 3D object detection, and 3D semantic segmentation. Key metrics from linear SVM evaluations on the ModelNet40 dataset indicate significant accuracy improvements. Moreover, STRL-pre-trained models exhibit strong generalizability when applied to distant domains, showing promising results even when fine-tuned on various downstream tasks.

Key Findings

1. Advancement Over Previous Methods: STRL surpasses existing self-supervised methods in performance metrics across synthetic and real-world benchmarks without the need for complex operations or architectural designs.
2. Simplicity and Efficiency: With a simple yet effective learning strategy, STRL successfully demonstrates robust self-supervised learning, emphasizing the importance of spatio-temporal augmentation.
3. Generalizability: Models pre-trained with STRL on either natural or synthetic datasets can be transferred effectively across domains. Remarkably, the transferability from natural to synthetic tasks, and vice versa, underscores the framework's broad applicability.
4. Data Efficiency: The paper emphasizes the crucial role of data diversity in pre-training efficacy over sheer volume, aligning with recent findings in 2D representation learning.

Conclusion and Future Directions

This work significantly contributes to self-supervised learning in 3D point cloud analysis by introducing a method that effectively utilizes spatio-temporal contexts. STRL has the potential to set the stage for more integrated approaches to 3D scene understanding, including applications in complex environments requiring holistic interpretations.

Future research could explore deeper integration with advanced 3D analysis tasks, encompassing more varied data types and domains, potentially incorporating additional modalities such as textures and color information. The simplicity and robustness of STRL make it an attractive candidate for further development in the pursuit of comprehensive 3D understanding systems.