Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark (1704.03847v1)

Abstract: This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

• The paper presents semantic3D.net as a benchmark for 3D point cloud classification using over four billion manually labeled points.
• It evaluates three baseline methods—2D image conversion, 3D covariance features, and 3D CNNs—highlighting the effectiveness of deep learning techniques.
• The benchmark sets a foundation for future research to refine 3D classification models and improve computational efficiency.

Overview of "semantic3D.net: A New Large-scale Point Cloud Classification Benchmark"

The paper introduces "semantic3D.net," a large-scale benchmark for 3D point cloud classification, designed to facilitate the advancement of data-driven approaches such as deep learning in 3D labeling tasks. The dataset comprises over four billion manually labeled 3D points spread across various urban scenes, addressing the data scarcity that has previously hindered breakthroughs in point cloud interpretation with deep convolutional neural networks (CNNs).

Dataset Description

The semantic3D.net dataset consists of detailed point clouds captured using terrestrial laser scanners, guaranteeing high density and completeness. It spans diverse outdoor environments, including urban and rural scenarios such as churches, streets, and castles, classified into eight semantic categories. This dataset is notable for its scale and quality, providing a valuable resource for training deep learning models that require extensive labeled data.

Baseline Methods and Preliminary Submissions

The paper outlines three baseline methods:

1. 2D Image Baseline: Converts 3D scans to 2D images using cube mapping, applying established 2D semantic segmentation techniques like associative hierarchical fields.
2. 3D Covariance Baseline: Utilizes multi-scale features directly from the 3D data and employs a random forest classifier, leveraging geometric properties via covariance-based feature extraction.
3. 3D CNN Baseline: Applies 3D deep learning on voxelized representations of point clouds, using a VGG-like architecture to accommodate varying point densities.

Initial submissions to the benchmark, using advanced CNN methodologies, have demonstrated performance enhancements over traditional approaches, signifying the potential of deep networks in processing large-scale 3D data.

Results and Implications

The benchmark results highlight the superiority of 3D CNN methods in certain aspects, although computational demands remain considerable. This suggests a growing effectiveness of deep learning strategies in 3D point cloud classification, contingent on adequate data availability for training.

Implications for Future Research

The introduction of semantic3D.net represents a substantial contribution to 3D point cloud research. It holds the promise to propel advancements in AI methods capable of interpreting complex 3D structures. Future research will likely explore more sophisticated network architectures to handle the intricacies of 3D data and improve real-time processing capabilities.

Conclusion

semantic3D.net establishes a comprehensive benchmark that addresses critical gaps in 3D point cloud training resources. By fostering more extensive empirical comparisons, it aims to guide the development of novel algorithms, enhancing the accuracy and computational efficiency of 3D classification methodologies in both academic and practical applications. The benchmark sets a strong foundation for further innovation in spatial data analysis with neural networks and other machine learning techniques.

This paper and the semantic3D.net benchmark are expected to serve as pivotal tools for researchers striving to unlock the full potential of deep learning in the field of 3D point cloud analysis.