Offset attention with seed generation for point cloud completion

Abstract Point cloud data acquired through 3D scanning is frequently subject to fragmentation due to the constraints of the scanner’s field of view and occlusions within the scanned object. The ensuing incompleteness in the data can significantly degrade the accuracy of subsequent computational tasks. Traditional methods for predicting complete point clouds from these fragments often fail to capture the fine-grained local details, leading to inaccurate reconstructions. In this work, we introduce a novel neural network architecture designed for point cloud completion that addresses these limitations.Our network accepts an incomplete point cloud and employs a multi-scale feature extraction module, which integrates an offset attention mechanism alongside a feature aggregation module operating across various scales. This dual approach significantly bolsters the network’s capacity to discern both local and global features inherent in the point cloud data. Furthermore, we incorporate a seed generation module within our missing point cloud generator, harnessing a hierarchical feature pyramid network to forecast the entirety of the point cloud. This innovative strategy allows our network to accurately predict the structure of missing regions.Empirical evaluations conducted on the Shapenet-Part and ModelNet10 datasets substantiate the efficacy of our proposed methodology. Our approach outperforms the state-of-the-art PF-Net algorithm, achieving a remarkable reduction in chamfer distance by 16.15$\%$ and 41.87$\%$ on the respective datasets. Visual inspection of the results underscores the robust generalization capabilities of our algorithm, which is particularly evident in scenarios with limited dataset sizes. It adeptly predicts the contours of the missing regions and synthesizes a more comprehensive point cloud shape.