SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer (2207.10315v1)

Abstract: Point cloud completion has become increasingly popular among generation tasks of 3D point clouds, as it is a challenging yet indispensable problem to recover the complete shape of a 3D object from its partial observation. In this paper, we propose a novel SeedFormer to improve the ability of detail preservation and recovery in point cloud completion. Unlike previous methods based on a global feature vector, we introduce a new shape representation, namely Patch Seeds, which not only captures general structures from partial inputs but also preserves regional information of local patterns. Then, by integrating seed features into the generation process, we can recover faithful details for complete point clouds in a coarse-to-fine manner. Moreover, we devise an Upsample Transformer by extending the transformer structure into basic operations of point generators, which effectively incorporates spatial and semantic relationships between neighboring points. Qualitative and quantitative evaluations demonstrate that our method outperforms state-of-the-art completion networks on several benchmark datasets. Our code is available at https://github.com/hrzhou2/seedformer.

SeedFormer: Advancements in Point Cloud Completion Using Patch Seeds and Upsample Transformer

The completion of 3D point clouds represents a pivotal theme within the field of computer vision, especially given the increasing reliance on LiDAR scanners and RGB-D cameras for 3D object and scene understanding. However, inherent challenges such as data sparsity and incompleteness of raw point clouds due to occlusions and limited sensor resolutions necessitate robust point cloud completion methods. The paper "SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer" explores this challenge, proposing a method that showcases significant improvements in the accuracy of point cloud completions, using novel techniques such as Patch Seeds and the Upsample Transformer.

SeedFormer Overview

SeedFormer is a model developed to enhance the detail preservation and completeness of point clouds. Two key innovations distinguish this model: the introduction of Patch Seeds for shape representation and the Upsample Transformer for point generation.

1. Patch Seeds: Unlike prior methodologies that utilized a global feature vector for shape representation, SeedFormer introduces Patch Seeds, which encapsulate regional information through local patterns. This approach aims to maintain regional information—critical for capturing the finer details that globally pooled features might overlook. Patch Seeds thus act as a more granular and precise feature representation that guides the network toward reconstructing complete point clouds more reliably.
2. Upsample Transformer: Augmenting the typical transformer structure, the Upsample Transformer effectively synthesizes the spatial and semantic context of points within their local neighborhoods. By treating point cloud completions in a coarse-to-fine manner, the Upsample Transformer leverages spatial attention mechanisms to ensure that newly generated points are semantically coherent with their neighboring points, enhancing overall detail fidelity.

Numerical and Qualitative Performance

Quantitative assessments indicate that SeedFormer surpasses the current leading models in terms of L1 Chamfer Distance across several challenging datasets, including PCN and ShapeNet. Specifically, SeedFormer achieves an average Chamfer Distance of 6.74 on the PCN dataset, indicating a lower average error in point reconstructions compared to its predecessors. The application of SeedFormer to the ShapeNet-55 and ShapeNet-34 datasets further illustrates its superior generalization capabilities not only on seen but also on novel object categories.

In visual demonstrations, SeedFormer consistently yields point clouds with better-defined structures and details. While other methods offer reasonably complete reconstructions, SeedFormer's outputs are distinguishable by their morphological correctness and fidelity to the original object shapes.

Implications and Future Directions

SeedFormer suggests promising implications for practical scenarios in robotics, autonomous navigation, and AR/VR environments by addressing common real-world challenges such as partial occlusions. The use of Patch Seeds and Attention-based mechanisms point to potential avenues for integrating additional sensory inputs or data augmentation techniques to refine and extend these methods. Moreover, the transformer-based enhancements to point generation could find wider application in other areas of 3D data processing and synthesis.

As transformer architectures continue to evolve, there is scope to further optimize SeedFormer, possibly trading computational overheads for performance, considering aspects like dynamic context aggregation and continuous shape refinement.

Overall, SeedFormer marks a substantial step towards solving the intricacies of point cloud completion, embodying an effective integration of local attention and shape-preserving mechanisms. By delivering enhanced precision and structural fidelity, it sets the stage for the next generation of 3D data analysis models.