A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors (2312.01262v1)

Abstract: Existing state-of-the-art 3D point cloud understanding methods merely perform well in a fully supervised manner. To the best of our knowledge, there exists no unified framework that simultaneously solves the downstream high-level understanding tasks including both segmentation and detection, especially when labels are extremely limited. This work presents a general and simple framework to tackle point cloud understanding when labels are limited. The first contribution is that we have done extensive methodology comparisons of traditional and learned 3D descriptors for the task of weakly supervised 3D scene understanding, and validated that our adapted traditional PFH-based 3D descriptors show excellent generalization ability across different domains. The second contribution is that we proposed a learning-based region merging strategy based on the affinity provided by both the traditional/learned 3D descriptors and learned semantics. The merging process takes both low-level geometric and high-level semantic feature correlations into consideration. Experimental results demonstrate that our framework has the best performance among the three most important weakly supervised point clouds understanding tasks including semantic segmentation, instance segmentation, and object detection even when very limited number of points are labeled. Our method, termed Region Merging 3D (RM3D), has superior performance on ScanNet data-efficient learning online benchmarks and other four large-scale 3D understanding benchmarks under various experimental settings, outperforming current arts by a margin for various 3D understanding tasks without complicated learning strategies such as active learning.

Understanding 3D Scenes with Efficient Data Labeling

Overview

The new framework discussed herein addresses a crucial challenge in 3D point cloud understanding: how do you efficiently parse complex scenes when labels are sparse? This is a common problem in applications like autonomous driving and industrial robotics, where it's neither feasible nor practical to annotate massive amounts of data. The proposed framework tackles this by integrating traditional and learned 3D descriptors to enhance learning from limited data.

Methodology Approach

At the heart of this paper is a review of various 3D descriptors, tested for over-segmentation and 3D scene understanding. The paper finds that traditional descriptors, like those based on Point Feature Histograms (PFH), still hold their own against newer learning-based descriptors, especially when it comes to generalization and robustness to domain shifts.

The paper introduces an adapted PFH descriptor, which is shown to be effective across different settings. Moreover, this adaptation of PFH and a new contrastive learning-based descriptor are implemented in a region merging process that takes both low-level geometric cues and high-level semantic relationships into account.

Data-Efficient Learning Framework

The framework named WeakLabel-3DNet is composed of multiple strategies and modules working cohesively:

• Region Merging: It is used to combine regions in point clouds based on feature similarities. This plays a key role in efficient learning as it leans on the network's predictions and traditional geometry descriptors to form larger, more meaningful segments for further processing.
• Network Modules: This includes self-supervised learning schemes and a data augmentation plan that propagates weak labels to similar regions, thus optimizing network learning under limited annotations.
• Object Detection: Building upon instance segmentation successes, the framework fine-tunes object detection by utilizing axis-aligned bounding boxes from instance segmentation results, demonstrating a significant leap in weakly supervised object detection accuracy.

Performance and Adaptability

The framework was tested extensively across various large-scale real-world datasets both indoors (like ScanNet and S3DIS) and outdoors (like SemanticKITTI). In scenarios with extremely limited labels (as low as 0.2%), the framework demonstrated superior performance compared to other methods that rely on active learning, self-training approaches, or pre-training strategies.

Furthermore, the ability to transfer learned models from one dataset or domain to another with minimal loss of accuracy underscore the framework's adaptability and robustness. This is particularly important for real-world applications that encounter diverse environments and conditions.

Conclusion

The outcomes of this research are crucial to advancing real-world implementations of 3D scene understanding systems that are both efficient and robust. The cross-domain generalization and efficient use of limited labels set the stage for further innovation and optimization, pointing toward a future of intelligent systems that can learn more from less and adapt swiftly to new environments.

The framework not only stands out in terms of performance but also provides valuable insights into the use of traditional geometric descriptors in harmony with modern learning-based strategies. This synergy could pave the way for new research directions, blending the best of both approaches to further push the limits of 3D scene parsing technologies.