PCN: Point Completion Network (1808.00671v3)

Abstract: Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.

• The paper presents a novel encoder-decoder architecture that processes unordered 3D point clouds to recover complete object shapes with high accuracy.
• The methodology employs a coarse-to-fine multistage decoder using Chamfer and Earth Mover's distances to refine global and local geometric details.
• Experiments on ShapeNet and KITTI demonstrate that PCN outperforms existing methods, offering robust performance in noisy and occluded settings.

Point Completion Network (PCN): A Formal Summary

Introduction

The paper, "PCN: Point Completion Network," proposes a novel approach to address shape completion, a critical problem in computer vision and robotics applications. Shape completion aims to infer the complete geometry of objects from partial observations. Current methods face limitations like high computational costs, dependency on class-specific characteristics, and loss of geometric details due to voxelization. The Point Completion Network (PCN) addresses these issues by operating directly on 3D point clouds without intermediate voxelization, semantic class constraints, or assumptions on the underlying structural properties of the shapes.

Methodology

PCN is an encoder-decoder network designed to operate efficiently on raw, unordered point clouds. The pipeline comprises three main components: the encoder, the multistage decoder, and a loss function that handles unordered point sets.

1. Encoder: The encoder abstracts the input point cloud into a feature vector. The design extends PointNet by incorporating two stacked PointNet layers to achieve permutation invariance and noise robustness. This design extracts both local and global geometric information. The encoder processes the input point cloud through shared multi-layer perceptrons and max pooling operations to generate a global feature vector summarizing the input geometry.
2. Multistage Decoder: The decoder generates the output point cloud through a coarse-to-fine approach. It first produces a coarse point set capturing the global structure, then refines it locally to generate a detailed point cloud. This two-stage generation leverages the capabilities of both fully-connected networks and folding-based methods, ensuring high resolution and efficiently parameterized outputs.
3. Loss Function: The loss function employs Chamfer Distance (CD) and Earth Mover's Distance (EMD) for training, both of which are permutation invariant. CD is used to compute the distance between the generated and ground truth point clouds. Due to computational concerns, EMD is only used for the coarse output stage, while CD is used for the detailed output.

Experiments and Results

The evaluation of PCN was conducted on synthetic datasets from ShapeNet and real-world LiDAR scans from the KITTI dataset.

1. Synthetic Data Evaluation: PCN was tested against existing methods including 3D-EPN and PointNet++ variants. The evaluation metrics included Chamfer Distance and Earth Mover's Distance. PCN exhibited superior performance across multiple object categories while maintaining significantly fewer parameters. Robustness to noise and varying levels of occlusion was also demonstrated, showing only a gradual increase in errors, indicative of the model's resilience.
2. Real-World Data Evaluation: PCN was applied to car point clouds from KITTI LiDAR scans. Without fine-tuning, PCN produced consistent and accurate completions, significantly improving downstream tasks like point cloud registration. Evaluation metrics for real-world data focused on fidelity, minimal matching distance (MMD), and output consistency across consecutive frames. PCN's completions facilitated more accurate registrations, highlighting its practical applicability in real-time systems.

Implications and Future Directions

The presented PCN architecture has several significant implications:

• Practical Applicability: By directly operating on point clouds and avoiding voxelization, PCN reduces memory costs and computational requirements, making it suitable for real-time applications. This is especially crucial in fields like autonomous driving and robotics.
• Generalizability: The model's ability to generalize to unseen object categories suggests a robust underlying shape prior, making it applicable to diverse real-world scenarios.
• Scalability and Extension: Future research can explore extending PCN to more complex environments like entire scenes or integrating it into more sophisticated systems for tasks such as SLAM.

Conclusion

PCN introduces an efficient, high-resolution, learning-based method for shape completion that leverages raw point clouds. Its innovative network architecture and multistage decoding process address the shortcomings of existing volumetric and point-based methods. The ability to generalize across different object categories and robustness to real-world noise and occlusions underline its potential for practical deployment. Future work can build on this foundation to further improve scalability and application range, cementing PCN's place in advanced computer vision and robotics tasks.

The insights and results presented by this paper mark a significant advancement in the domain of 3D shape completion, paving the way for more robust and efficient point cloud processing methodologies.