PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surface
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914715706327040 |
|---|---|
| author | Lim, Sangwon El-Basyouny, Karim Yang, Yee Hong |
| author_facet | Lim, Sangwon El-Basyouny, Karim Yang, Yee Hong |
| contents | While recent advancements in deep-learning point cloud upsampling methods have improved the input to intelligent transportation systems, they still suffer from issues of domain dependency between synthetic and real-scanned point clouds. This paper addresses the above issues by proposing a new ray-based upsampling approach with an arbitrary rate, where a depth prediction is made for each query ray and its corresponding patch. Our novel method simulates the sphere-tracing ray marching algorithm on the neural implicit surface defined with an unsigned distance function (UDF) to achieve more precise and stable ray-depth predictions by training a point-transformer-based network. The rule-based mid-point query sampling method generates more evenly distributed points without requiring an end-to-end model trained using a nearest-neighbor-based reconstruction loss function, which may be biased towards the training dataset. Self-supervised learning becomes possible with accurate ground truths within the input point cloud. The results demonstrate the method's versatility across domains and training scenarios with limited computational resources and training data. Comprehensive analyses of synthetic and real-scanned applications provide empirical evidence for the significance of the upsampling task across the computer vision and graphics domains to real-world applications of ITS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_08755 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surface Lim, Sangwon El-Basyouny, Karim Yang, Yee Hong Computer Vision and Pattern Recognition Graphics I.4.5; I.3.5 While recent advancements in deep-learning point cloud upsampling methods have improved the input to intelligent transportation systems, they still suffer from issues of domain dependency between synthetic and real-scanned point clouds. This paper addresses the above issues by proposing a new ray-based upsampling approach with an arbitrary rate, where a depth prediction is made for each query ray and its corresponding patch. Our novel method simulates the sphere-tracing ray marching algorithm on the neural implicit surface defined with an unsigned distance function (UDF) to achieve more precise and stable ray-depth predictions by training a point-transformer-based network. The rule-based mid-point query sampling method generates more evenly distributed points without requiring an end-to-end model trained using a nearest-neighbor-based reconstruction loss function, which may be biased towards the training dataset. Self-supervised learning becomes possible with accurate ground truths within the input point cloud. The results demonstrate the method's versatility across domains and training scenarios with limited computational resources and training data. Comprehensive analyses of synthetic and real-scanned applications provide empirical evidence for the significance of the upsampling task across the computer vision and graphics domains to real-world applications of ITS. |
| title | PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surface |
| topic | Computer Vision and Pattern Recognition Graphics I.4.5; I.3.5 |
| url | https://arxiv.org/abs/2310.08755 |