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arXiv · 2407.04476

Rethinking Data Input for Point Cloud Upsampling

Tongxu Zhang
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Abstract

Point cloud upsampling is crucial for tasks like 3D reconstruction. While existing methods rely on patch-based inputs, and there is no research discussing the differences and principles between point cloud model full input and patch based input. Ergo, we propose a novel approach using whole model inputs i.e. Average Segment input. Our experiments on PU1K and ABC datasets reveal that patch-based inputs consistently outperform whole model inputs. To understand this, we will delve into factors in feature extraction, and network architecture that influence upsampling results.

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Tongxu Zhang. 2024-07-05. Rethinking Data Input for Point Cloud Upsampling. https://doi.org/10.1007/978-3-031-94898-5_3

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