PPFNet: Global Context Aware Local Features for Robust 3D Point Matching (1802.02669v2)

Abstract: We present PPFNet - Point Pair Feature NETwork for deeply learning a globally informed 3D local feature descriptor to find correspondences in unorganized point clouds. PPFNet learns local descriptors on pure geometry and is highly aware of the global context, an important cue in deep learning. Our 3D representation is computed as a collection of point-pair-features combined with the points and normals within a local vicinity. Our permutation invariant network design is inspired by PointNet and sets PPFNet to be ordering-free. As opposed to voxelization, our method is able to consume raw point clouds to exploit the full sparsity. PPFNet uses a novel $\textit{N-tuple}$ loss and architecture injecting the global information naturally into the local descriptor. It shows that context awareness also boosts the local feature representation. Qualitative and quantitative evaluations of our network suggest increased recall, improved robustness and invariance as well as a vital step in the 3D descriptor extraction performance.

• The paper introduces PPFNet, which learns globally informed local descriptors for robust 3D point matching without voxelization.
• It utilizes a novel N-tuple loss function that enhances feature separability through simultaneous multi-pair evaluations.
• PPFNet demonstrates superior recall and efficiency on benchmarks, effectively handling variations in point density and rotation.

Overview of PPFNet: Global Context Aware Local Features for Robust 3D Point Matching

The paper introduces PPFNet, a neural network designed to enhance 3D point matching by learning a globally informed local feature descriptor for unorganized point clouds. PPFNet combines point pair features (PPF) with a network architecture that draws from PointNet to achieve permutation invariance. This approach foregoes voxelization, instead relying on raw point clouds to leverage sparsity.

Key Contributions

1. Representation and Network Design: PPFNet builds upon point pair features merged with local geometrical data, such as points and normals. This allows the extraction of local features that are aware of the global context, improving robustness in feature representation. The network employs a permutation invariant architecture inspired by PointNet, enabling it to process unordered point clouds effectively.
2. Novel Loss Function: The introduction of the N-tuple loss is a major contribution, extending beyond traditional pair or triplet loss functions. By embedding multiple matching and non-matching pairs into a Euclidean space simultaneously, the network greatly improves feature separability.
3. Performance: Evaluations indicate that PPFNet achieves superior recall, robustness, and invariance over existing methods. The system is particularly adept at managing variations in point density and rotational changes, making it highly applicable in real-world scenarios.

Detailed Analysis

Input Encoding and Feature Extraction

PPFNet encodes local geometry using point pair features combined with points and normals. This encoding involves calculating the PPF descriptor, which is invariant to Euclidean transformations, thus enhancing robustness against rotations. The network processes this input using a series of parallel PointNets, followed by a max-pooling operation that introduces global contextual awareness into local descriptors. The final output is derived from MLP layers fusing global and local features into a compact descriptor.

Impact of N-tuple Loss

The N-tuple loss function is a key innovation, designed to improve feature learning by addressing the inherent many-to-many nature of correspondences in 3D point clouds. By evaluating the distances between features of matching pairs in a combinatorial fashion, this loss function ensures robust and discriminative feature learning.

Contributions to 3D Vision

The methodological advancements offered by PPFNet have significant implications for tasks in 3D vision, such as object recognition, scene reconstruction, and SLAM. The ability to process raw point clouds without voxelization preserves computational efficiency and sparsity, facilitating faster performance compared to competing methods like 3DMatch.

Evaluation and Results

PPFNet demonstrates strong quantitative results on the 3DMatch benchmark, notably outperforming both traditional and recent deep learning-based descriptors in terms of mean recall. The use of only 2048 sample patches, compared to the larger datasets required by previous state-of-the-art models, underscores its efficiency. Additionally, robustness tests against varying point densities affirm the model’s applicability to diverse real-world conditions.

Future Directions

One noted limitation is the quadratic memory consumption, which restricts scalability. Future work could focus on mitigating this through more advanced memory management strategies, potentially allowing for the processing of denser point clouds.

Conclusion

The research presented in this paper advances the domain of 3D point matching through the introduction of PPFNet. By leveraging a novel approach to feature learning that incorporates global context awareness and robust loss function design, PPFNet sets a new standard in efficiency and accuracy for learning 3D local descriptors. This work opens pathways for further exploration in efficient 3D data processing and offers a significant contribution to computational geometry and AI-driven tasks in 3D environments.