Learning Compact Geometric Features (1709.05056v1)

Abstract: We present an approach to learning features that represent the local geometry around a point in an unstructured point cloud. Such features play a central role in geometric registration, which supports diverse applications in robotics and 3D vision. Current state-of-the-art local features for unstructured point clouds have been manually crafted and none combines the desirable properties of precision, compactness, and robustness. We show that features with these properties can be learned from data, by optimizing deep networks that map high-dimensional histograms into low-dimensional Euclidean spaces. The presented approach yields a family of features, parameterized by dimension, that are both more compact and more accurate than existing descriptors.

• The paper presents a deep learning method to extract compact geometric features from point clouds, mapping local geometric information into low-dimensional spaces using spherical histograms and triplet loss.
• Compact Geometric Features (CGF) outperform traditional descriptors in accuracy and efficiency, achieving 72% recall on the Redwood benchmark and significantly reducing nearest-neighbor query times.
• This work represents a shift to data-driven descriptor learning, enabling advances in robotics, computer vision, and 3D reconstruction while suggesting future research in dynamic scenes and alternative network architectures.

Learning Compact Geometric Features

The paper "Learning Compact Geometric Features" by Marc Khoury, Qian-Yi Zhou, and Vladlen Koltun presents an innovative method for deriving local geometric descriptors from point clouds via learned embeddings. This research addresses a long-standing issue within feature design for geometric registration: the need for descriptors that are simultaneously discriminative, compact, and robust.

Summary

The authors introduce a technique that leverages deep learning to extract features from unstructured point clouds, synthesizing high-dimensional histograms of local geometries into low-dimensional Euclidean spaces. This method creates a suite of features called Compact Geometric Features (CGF), offering a parameterizable trade-off between precision and dimensionality. The CGFs are shown to outperform traditional, hand-crafted descriptors, such as PFH, FPFH, SHOT, and USC, in terms of accuracy and computational efficiency.

Technical Approach

The core idea is to learn an embedding of spherical histograms, which represent point cloud neighborhoods, to compact feature spaces amenable for efficient nearest-neighbor searches. The process begins by parameterizing each point through local geometric contexts structured as spherical histograms. These histograms are then input into a deep neural network optimized using a triplet loss technique to map them to a lower-dimensional feature space, preserving the essential geometric information while facilitating rapid computation.

The training and evaluation of this model involve various datasets, with a focus on generalization across domains, including SceneNN and laser scan data. The robustness of CGFs is demonstrated via experiments that showcase significantly reduced nearest-neighbor query times and improved match precision compared to competing descriptors.

Results and Implications

Quantitatively, CGFs achieve a recall of 72% on the Redwood benchmark, surpassing contemporary descriptors and setting a new standard for geometric feature extraction. The reduction in dimensionality without loss of descriptive power enables faster processing and more accurate registration, directly benefiting robotics, computer vision, and 3D reconstruction applications.

The use of deep learning for descriptor learning exemplifies a paradigm shift from manual feature crafting towards data-driven optimization. This shift promises advances in other areas requiring feature extraction, prompting future exploration into further reductions of computational requirements and enhancing feature invariance across varied geometric conditions.

Future Directions

Potential future developments from this work could include extending CGFs to accommodate dynamic and deformable scenes, which are common in real-world applications. Additionally, exploring alternative network architectures and loss functions might yield further improvements in embedding quality and feature compactness. Integration with broader AI systems, exploiting learned geometric features for high-level scene understanding, represents a promising avenue for future AI innovations.

In conclusion, "Learning Compact Geometric Features" contributes substantially to the field by providing a robust toolkit based on learned descriptors, advancing automatic feature extraction's breadth and practicality in 3D geometry contexts. This work serves as a compelling case paper in applying machine learning techniques to classic algorithmic problems.