POCO: Point Convolution for Surface Reconstruction (2201.01831v2)

Abstract: Implicit neural networks have been successfully used for surface reconstruction from point clouds. However, many of them face scalability issues as they encode the isosurface function of a whole object or scene into a single latent vector. To overcome this limitation, a few approaches infer latent vectors on a coarse regular 3D grid or on 3D patches, and interpolate them to answer occupancy queries. In doing so, they loose the direct connection with the input points sampled on the surface of objects, and they attach information uniformly in space rather than where it matters the most, i.e., near the surface. Besides, relying on fixed patch sizes may require discretization tuning. To address these issues, we propose to use point cloud convolutions and compute latent vectors at each input point. We then perform a learning-based interpolation on nearest neighbors using inferred weights. Experiments on both object and scene datasets show that our approach significantly outperforms other methods on most classical metrics, producing finer details and better reconstructing thinner volumes. The code is available at https://github.com/valeoai/POCO.

Implicit Neural Networks for Scalable Surface Reconstruction

The paper under discussion presents an innovative approach to surface reconstruction using implicit neural networks. The primary focus of the paper is to address the scalability issues observed in traditional neural network architectures for surface reconstruction from point clouds. Earlier methods typically encoded the isosurface function of entire objects or scenes into singular latent vectors. This approach inherently faced challenges in handling large and complex scenes due to its limited scalability.

To resolve these scalability problems, this paper introduces an alternative method utilizing point cloud convolutions, wherein latent vectors are computed at each input point rather than across a grid or patch. The method emphasizes a learning-based interpolation on the nearest neighbors, leveraging inferred weights to answer occupancy queries. This technique maintains a closer association with input points, focusing the latent vectors near the surface, where precision is paramount.

Key Contributions

The key contributions of the paper extend beyond simple architectural adjustments and propose a fundamentally different approach to latent vector computation and interpolation for occupancy queries:

1. Point-Convolutional Approach: By attaching features directly to the input points, the method retains spatial information closer to the original data, allowing the network to operate with higher precision around the critical surface areas.
2. Learning-Based Interpolation: The approach employs a learned interpolation for the latent vectors, focusing on maximum relevance through neighbor importance rather than relying on a static interpolation schema.
3. Test-Time Augmentation: To improve handling of higher point densities, a test-time augmentation strategy is proposed that applies data augmentation at the feature level, improving the receptive field size without significantly increasing computational overhead.
4. Scalability: The method’s ability to handle scenes of arbitrary size and density without dynamically resizing or modifying patches represents a significant step forward in the scalability of surface reconstruction architectures.

Experimental Validation

The experiments conducted provide comprehensive validation across object and scene datasets, demonstrating significant improvements compared to existing methods such as ConvONet, PointNet, and others. Notably, the method achieves finer detail reconstruction and presents robustness against domain shifts and variations in input density.

• On datasets like ShapeNet and Synthetic Rooms, the proposed approach outperforms its contemporaries on metrics such as IoU and Chamfer Distance, showcasing its ability to maintain higher levels of detail and accuracy.
• The performance gains are attributed to the architecture’s tacit adaptation to high-density scenarios and the flexibility introduced via learned interpolation.

Implications and Future Scope

The integration of point cloud convolutions with implicit neural networks suggests a promising direction for future research, indicative of enhanced potential for real-time scene understanding and reconstruction. The method has applications across various domains, including augmented reality, digital twin creation, and autonomous navigation, where accurate and scalable surface reconstruction is critical.

Future work may focus on further optimizing computational efficiency to enhance real-time application capabilities or extending the architectural principles to other forms of 3D data and representation. Large-scale real-world applicability will be especially vital, and continued optimization in latent vector handling and network training will be pivotal.

In conclusion, this paper presents significant advancements in the field of surface reconstruction through implicit neural networks, offering a scalable, precise, and efficient avenue for handling complex 3D data and surfaces. Such advancements drive the intersection of computer vision and deep learning forward, promising richer and more comprehensive models of the three-dimensional world.