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SSRNet: Scalable 3D Surface Reconstruction Network.
IEEE Transactions on Visualization and Computer Graphics
|July 25, 2022
Summary
This study introduces SSRNet, a scalable deep learning method for 3D surface reconstruction from large point clouds. It efficiently processes complex data using an octree structure, achieving state-of-the-art results.
Area of Science:
- Computer Vision
- 3D Geometry Processing
- Machine Learning
Background:
- Learning-based surface reconstruction offers high expressiveness but struggles with scalability for large point clouds.
- Existing methods often fail to efficiently process extensive 3D datasets.
Purpose of the Study:
- To propose a novel, scalable learning-based 3D surface reconstruction method for large-scale point clouds.
- To address the limitations of existing methods in terms of processing efficiency and scalability.
Main Methods:
- Developed SSRNet, a method utilizing an octree structure for scalable reconstruction.
- Implemented a parallel processing pipeline that divides point clouds into local parts.
- Constructed local geometric features for octree vertices, capturing the relation to the implicit surface.
Main Results:
- SSRNet demonstrates state-of-the-art performance in 3D surface reconstruction.
- The method achieves outstanding efficiency in processing large-scale point clouds.
- A lightweight version of SSRNet offers a five-fold speed increase with maintained performance.
Conclusions:
- SSRNet provides a scalable and efficient solution for learning-based 3D surface reconstruction.
- The octree-based approach and local feature extraction prevent overfitting and ensure generalization.
- SSRNet significantly advances the state-of-the-art in processing large-scale 3D data.
