Learnable Point Cloud Sampling Considering Seed Point for Neural Surface Reconstruction
Reconstruction of surfaces from point clouds is essential in numerous practical applications. An approach in which neural fields are trained as surface representations from point clouds has garnered significant interest in recent years. However, these techniques present scalability issues to large s...
Saved in:
| Main Authors: | Kohei Matsuzaki, Keisuke Nonaka |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10804151/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Fitting Geometric Shapes to Fuzzy Point Cloud Data
by: Vincent B. Verhoeven, et al.
Published: (2025-01-01) -
Semantic Segmentation and Reconstruction of Indoor Scene Point Clouds
by: HAO, W., et al.
Published: (2024-08-01) -
A partial overlapping point cloud registration method based on dynamic feature matching
by: Hui DU, et al.
Published: (2021-04-01) -
A Registration Method Based on Ordered Point Clouds for Key Components of Trains
by: Kai Yang, et al.
Published: (2024-12-01) -
Irregular seeds DEM parameters prediction based on 3D point cloud and GA-BP-GA optimization
by: Yuling Shao, et al.
Published: (2025-01-01)