# SSRNet: Scalable 3D Surface Reconstruction Network

arXiv: https://arxiv.org/abs/1911.07401 · HTML: https://mlsanity.com/paper/1911.07401/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 30 of 35)

- [Neural Dual Contouring](https://mlsanity.com/paper/2202.01999/) (2022)
- [Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors](https://mlsanity.com/paper/2204.10603/) (2022)
- [Surface Reconstruction from Point Clouds by Learning Predictive Context Priors](https://mlsanity.com/paper/2204.11015/) (2022)
- [CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimization](https://mlsanity.com/paper/2210.02757/) (2022)
- [Surface Reconstruction from Point Clouds: A Survey and a Benchmark](https://mlsanity.com/paper/2205.02413/) (2022)
- [Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment](https://mlsanity.com/paper/2305.11601/) (2023)
- [Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors](https://mlsanity.com/paper/2303.14505/) (2023)
- [Neural Shape Deformation Priors](https://mlsanity.com/paper/2210.05616/) (2022)
- [Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping](https://mlsanity.com/paper/2306.01405/) (2023)
- [Latent Partition Implicit with Surface Codes for 3D Representation](https://mlsanity.com/paper/2207.08631/) (2022)
- [Fast Learning of Signed Distance Functions from Noisy Point Clouds via Noise to Noise Mapping](https://mlsanity.com/paper/2407.14225/) (2024)
- [Deep Hybrid Self-Prior for Full 3D Mesh Generation](https://mlsanity.com/paper/2108.08017/) (2021)
- [DeepDT: Learning Geometry From Delaunay Triangulation for Surface Reconstruction](https://mlsanity.com/paper/2101.10353/) (2021)
- [GridPull: Towards Scalability in Learning Implicit Representations from 3D Point Clouds](https://mlsanity.com/paper/2308.13175/) (2023)
- [MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Step](https://mlsanity.com/paper/2411.01208/) (2024)
- [What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives](https://mlsanity.com/paper/2211.06009/) (2022)
- [DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling](https://mlsanity.com/paper/2402.08876/) (2024)
- [Object Wake-up: 3D Object Rigging from a Single Image](https://mlsanity.com/paper/2108.02708/) (2021)
- [Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds](https://mlsanity.com/paper/2206.06715/) (2022)
- [Scalable Surface Reconstruction with Delaunay-Graph Neural Networks](https://mlsanity.com/paper/2107.06130/) (2021)
- [Implicit Filtering for Learning Neural Signed Distance Functions from 3D Point Clouds](https://mlsanity.com/paper/2407.13342/) (2024)
- [Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors](https://mlsanity.com/paper/2410.19680/) (2024)
- [Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation](https://mlsanity.com/paper/2503.23670/) (2025)
- [LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies](https://mlsanity.com/paper/2111.15113/) (2021)
- [SuperUDF: Self-supervised UDF Estimation for Surface Reconstruction](https://mlsanity.com/paper/2308.14371/) (2023)
- [Procedural Generation of 3D Maize Plant Architecture from LIDAR Data](https://mlsanity.com/paper/2501.13963/) (2025)
- [NeuralMeshing: Differentiable Meshing of Implicit Neural Representations](https://mlsanity.com/paper/2210.02382/) (2022)
- [Isomorphic mesh generation from point clouds with multilayer perceptrons](https://mlsanity.com/paper/2210.14157/) (2022)
- [Tensorformer: Normalized Matrix Attention Transformer for High-quality Point Cloud Reconstruction](https://mlsanity.com/paper/2306.15989/) (2023)
- [Optimizing Implicit Neural Representations from Point Clouds via Energy-Based Models](https://mlsanity.com/paper/2311.02601/) (2023)

## Abstract (arXiv)

Existing learning-based surface reconstruction methods from point clouds are still facing challenges in terms of scalability and preservation of details on large-scale point clouds. In this paper, we propose the SSRNet, a novel scalable learning-based method for surface reconstruction. The proposed SSRNet constructs local geometry-aware features for octree vertices and designs a scalable reconstruction pipeline, which not only greatly enhances the predication accuracy of the relative position between the vertices and the implicit surface facilitating the surface reconstruction quality, but also allows dividing the point cloud and octree vertices and processing different parts in parallel for superior scalability on large-scale point clouds with millions of points. Moreover, SSRNet demonstrates outstanding generalization capability and only needs several surface data for training, much less than other learning-based reconstruction methods, which can effectively avoid overfitting. The trained model of SSRNet on one dataset can be directly used on other datasets with superior performance. Finally, the time consumption with SSRNet on a large-scale point cloud is acceptable and competitive. To our knowledge, the proposed SSRNet is the first to really bring a convincing solution to the scalability issue of the learning-based surface reconstruction methods, and is an important step to make learning-based methods competitive with respect to geometry processing methods on real-world and challenging data. Experiments show that our method achieves a breakthrough in scalability and quality compared with state-of-the-art learning-based methods.