Interpolated Convolutional Networks for 3D Point Cloud Understanding (1908.04512v1)

Abstract: Point cloud is an important type of 3D representation. However, directly applying convolutions on point clouds is challenging due to the sparse, irregular and unordered data structure. In this paper, we propose a novel Interpolated Convolution operation, InterpConv, to tackle the point cloud feature learning and understanding problem. The key idea is to utilize a set of discrete kernel weights and interpolate point features to neighboring kernel-weight coordinates by an interpolation function for convolution. A normalization term is introduced to handle neighborhoods of different sparsity levels. Our InterpConv is shown to be permutation and sparsity invariant, and can directly handle irregular inputs. We further design Interpolated Convolutional Neural Networks (InterpCNNs) based on InterpConv layers to handle point cloud recognition tasks including shape classification, object part segmentation and indoor scene semantic parsing. Experiments show that the networks can capture both fine-grained local structures and global shape context information effectively. The proposed approach achieves state-of-the-art performance on public benchmarks including ModelNet40, ShapeNet Parts and S3DIS.

• The paper introduces the InterpConv operation that uses discrete kernel weights with interpolation to handle irregular, sparse 3D point clouds.
• The proposed InterpCNN architecture achieves 93.0% accuracy on ModelNet40 and 84.0% mean IoU on ShapeNet Parts, surpassing previous methods.
• By avoiding voxelization, the method enhances computational efficiency and robustness, making it ideal for real-time 3D applications.

An Overview of Interpolated Convolutional Networks for 3D Point Cloud Understanding

The paper presents a novel approach to address the challenges inherent in applying convolutional operations directly to 3D point clouds. Traditional methods face difficulties due to the irregular, sparse, and unordered nature of point clouds. The authors introduce the 'Interpolated Convolution' (InterpConv) operation to overcome these barriers by employing discrete kernel weights alongside an interpolation function. This methodology allows for effective feature learning from point clouds without the need for transformation into voxel grids, which often results in a loss of geometric information.

Methodological Contributions

The primary innovation proposed is the InterpConv operation, which maintains the permutation and sparsity-invariant properties required for handling irregular point clouds. By using discrete kernel weights and interpolating point features to neighboring kernel-weight coordinates, the paper introduces a powerful mechanism for feature aggregation. A normalization term ensures invariance to varying neighborhood densities, which is critical given the sparsity typical of point cloud data.

The authors extend the InterpConv into a broader architectural framework termed Interpolated Convolutional Neural Networks (InterpCNNs). These networks are constructed to handle multiple point cloud tasks such as shape classification, part segmentation, and semantic parsing of indoor scenes.

Experimental Analysis

The proposed networks demonstrate state-of-the-art results across several benchmark datasets, including ModelNet40, ShapeNet Parts, and S3DIS. The classification network, leveraging multi-receptive-field InterpConv blocks akin to an Inception module, achieves an accuracy of 93.0% on ModelNet40, surpassing existing methods such as PointNet++ and DGCNN.

For part segmentation, applying InterpCNNs on ShapeNet Parts results in mean IoU of 84.0% over categories, demonstrating superior instance segmentation capabilities. Furthermore, the approach excels in semantic segmentation tasks on the S3DIS dataset, exhibiting high accuracy and mean IoU by intelligently capturing both local and contextual information.

Implications and Future Prospects

The paper's contributions highlight a significant improvement in processing speed and accuracy for point cloud operations. By avoiding the computational overhead associated with rasterization and voxel-based methods, the proposed networks cater to efficient real-time applications, a necessity in domains such as autonomous driving and robotics.

Theoretically, this work propels the understanding of utilizing discrete kernels in irregular spaces, a concept that could be explored further in more complex architectures or adapted for other tasks involving irregular data structures. The notion of integrating learnable kernel-weight coordinates fortifies the potential for even more versatile applications in 3D object recognition and segmentation.

Conclusion

In summary, the proposed InterpConv operation and the resultant InterpCNN architectures offer a novel pathway for understanding and processing 3D point clouds. With their demonstrated efficacy across various datasets and tasks, they present a compelling alternative to current state-of-the-art techniques. Future research may expand upon this foundation, exploring novel interpolation functions or integrating these methodologies into broader AI systems for enhanced perception in complex environments.