This paper introduces the Building-PCC dataset, a new real-world benchmark for evaluating deep learning-based point cloud completion methods on building data. The dataset consists of 50,000 building instances from two cities in the Netherlands, paired with corresponding partial point clouds from airborne LiDAR scans (AHN3 and AHN4) and complete ground truth point clouds sampled from manually reconstructed 3D building models.
The authors conduct a comprehensive evaluation of eight representative deep learning methods for point cloud completion, including PCN, FoldingNet, TopNet, GRNet, SnowflakeNet, PoinTr, AnchorFormer, and AdaPoinTr. The performance of these methods is assessed using the mean Chamfer Distance (CD-l1) and F-Score metrics.
The results show that PoinTr, AnchorFormer, and AdaPoinTr outperform other methods in terms of average CD-l1, with PoinTr achieving the best performance. AdaPoinTr excels in the F-Score metric, closely following PoinTr in CD-l1. However, the authors identify several key challenges faced by these methods when dealing with real-world building point clouds, including:
Imbalanced datasets: The uneven distribution of incomplete areas, such as building facades and roofs, can lead to models performing better on certain regions and worse on others.
Limitations on fine details: The methods struggle to accurately reproduce small building components, like chimneys and dormers, due to the low resolution of the predicted point clouds.
Over-smoothed sharp features: The methods tend to over-smooth sharp corners and edges of buildings, resulting in a loss of architectural details.
Normalization issues: Existing normalization techniques, which rely on aligning partial point clouds with ground truth data, may not be feasible in practical applications where ground truth is unavailable.
To address these challenges, the authors propose several potential solutions, such as developing synthetic datasets with diverse incomplete patterns, incorporating local plane distance into the loss function, and utilizing external data sources like GIS data to assist with normalization. These insights aim to guide future research and foster innovation in the field of 3D geoinformation applications.
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by Weixiao Gao,... at arxiv.org 04-25-2024
https://arxiv.org/pdf/2404.15644.pdfDeeper Inquiries