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NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance
Hanlin Chen, Chen Li, Yunsong Wang, Gim Hee Lee
TL;DR
Existing neural implicit reconstruction methods can lose fine detail because depth maps are over-smoothed and point clouds are sparse or noisy. NeuSG jointly optimizes neural implicit surfaces and 3D Gaussian Splatting, using thin Gaussians and neural normal guidance to produce more complete, detailed surfaces. On Tanks and Temples, it improves reconstruction over prior methods while requiring fewer computational hours than NAngelo-22 in the reported comparison, but it remains dependent on dense multi-view observations.
Problem
Existing neural implicit reconstruction methods lack fine details when relying on over-smoothed depth maps or sparse point clouds as geometry priors.
Method
NeuSG jointly optimizes NeuS and 3D Gaussian Splatting, using a scale regularizer to make Gaussians thin and neural-surface normals to refine them.
Results
NeuSG improves surface reconstruction, achieving an F1 score of 0.49 versus 0.38 for NeuS and 0.49 versus 0.5 for NAngelo-22 while using 16 versus 128 hours.
Takeaways & Limitations
The combined representations recover complete surfaces with intricate details, including fine structures and fewer extraneous surface elements.
Takeaways & Limitations
Accurate reconstruction depends on dense multi-view observations and may degrade with sparse or uneven image distributions.
Abstract
from arXiv · showhide
Existing neural implicit surface reconstruction methods have achieved impressive performance in multi-view 3D reconstruction by leveraging explicit geometry priors such as depth maps or point clouds as regularization. However, the reconstruction results still lack fine details because of the over-smoothed depth map or sparse point cloud. In this work, we propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces. The advantage of 3D Gaussian Splatting is that it can generate dense point clouds with detailed structure. Nonetheless, a naive adoption of 3D Gaussian Splatting can fail since the generated points are the centers of 3D Gaussians that do not necessarily lie on the surface. We thus introduce a scale regularizer to pull the centers close to the surface by enforcing the 3D Gaussians to be extremely thin. Moreover, we propose to refine the point cloud from 3D Gaussians Splatting with the normal priors from the surface predicted by neural implicit models instead of using a fixed set of points as guidance. Consequently, the quality of surface reconstruction improves from the guidance of the more accurate 3D Gaussian splatting. By jointly optimizing the 3D Gaussian Splatting and the neural implicit model, our approach benefits from both representations and generates complete surfaces with intricate details. Experiments on Tanks and Temples verify the effectiveness of our proposed method.
1. Introduction
Neural implicit reconstruction handles challenging multi-view scenes but can lose detail when guided by over-smoothed or sparse geometry. NeuSG addresses this by jointly optimizing neural surfaces and 3D Gaussian Splatting, using thin Gaussians and normal guidance to improve reconstruction.
- Motivation: Neural implicit methods struggle in large scenes with extensive textureless areas despite strong performance on simpler scenes.SDF-based methods use rendering supervision, but additional structural priors are needed for difficult regions.
- Motivation: MVS point clouds can be incomplete, noisy, and unreliable as geometry priors, especially around intricate details.Their uniform distribution and noisy geometry limit coverage and guidance quality.
- Proposed direction: 3D Gaussian Splatting provides dense, detailed point clouds, but Gaussian centers may lie inside surfaces rather than on them.NeuSG therefore introduces a scale regularizer that makes each Gaussian extremely thin by shrinking its smallest scaling factor toward zero.
- Contribution: NeuSG jointly optimizes NeuS and 3D Gaussian Splatting, using Gaussian point clouds to regulate NeuS and NeuS normals to refine the Gaussians.The framework also introduces scale and normal regularizers so thin Gaussian point clouds adhere closely to surfaces.
2. Related Work
Related work progresses from classical multi-view reconstruction and volumetric neural rendering toward faster, more detailed representations. NeuSG uses 3D Gaussian Splatting as a dense point-cloud source for complete and detailed surfaces, addressing limitations of existing priors and encodings.
- Multi-view surface reconstruction: Classical MVS estimates depth or voxel occupancy from multiple views before combining results into point clouds or surfaces.Depth-based methods use feature matching, while volumetric methods rely on voxel grids and color consistency.
- Neural rendering: NeRF and later neural rendering methods improve view synthesis but face substantial sampling costs and speed-quality trade-offs.Subsequent work uses encodings, spatial structures, and alternative representations to improve rendering efficiency or fidelity.
- 3D Gaussian Splatting: 3D Gaussian Splatting offers an unstructured, explicit, GPU-optimized representation with faster rendering and improved quality without neural components.NeuSG uses it to generate dense point clouds near surfaces for complete and detailed reconstruction.
- Neural surface reconstruction: Neural implicit surfaces improve surface prediction and view-synthesis quality, but auxiliary-data methods remain limited by point-cloud quality or lack sharp detail.Depth, segmentation, and sparse SfM supervision can build complete geometry while often producing overly smooth results.
3. Our Method
NeuSG combines NeuS and 3D Gaussian Splatting through joint optimization. Its Gaussian scale and normal regularization supplies geometric guidance while refining Gaussian point clouds with neural-surface information.
- Framework overview: NeuSG efficiently reconstructs complete and detailed scene surfaces from multi-view images using NeuS and 3D Gaussian Splatting.The framework overview covers both representations and their joint optimization.
- Framework overview: The method details scale and normal regularization for 3D Gaussians before jointly optimizing the Gaussian and implicit-surface representations.These components are presented as the core framework shown in Figure 2.
3.1. Preliminary
The preliminary formulation describes NeuS as an SDF-based implicit surface renderer and 3D Gaussian Splatting as an explicit Gaussian scene representation. Figure 2 summarizes how the framework combines surface optimization, Gaussian geometric constraints, and normal-based refinement.
- Neural implicit surfaces: NeuS represents surfaces as the zero level set of an SDF and converts SDF values into opacity for volume rendering.The intermediate SDF also supports Eikonal regularization for surface reconstruction.
- Neural implicit surfaces: NeuS integrates opacity-weighted colors along posed camera rays to render pixels from sampled 3D points.The color of each sample is predicted by an MLP, while α_i and T_i determine its rendering contribution.
- 3D Gaussian Splatting: 3D Gaussian Splatting represents a scene with Gaussians defined by covariance matrices and center points.Its covariance is parameterized through scaling and rotation, with scaling factors and quaternion rotations used for optimization.
- NeuSG framework: NeuSG combines implicit-surface optimization, Gaussian point-cloud geometric constraints, and Gaussian refinement through normal alignment.These three components are formalized in the framework overview.
- 3D Gaussian Splatting: Gaussian splatting transforms Gaussian covariances to camera planes for novel-view rendering and blends overlapping points by color and opacity.The representation includes position, spherical-harmonics color, opacity, quaternion rotation, and 3D scaling.
3.2. Regularization for 3D Gaussians
NeuSG regularizes 3D Gaussians so their centers approach the surface and their normals align with NeuS-predicted surface normals, producing more dependable detailed point-cloud guidance.
- 3D Gaussian Splatting provides dense point clouds with intricate geometry, but Gaussian centers may lie inside the surface and cannot directly serve as priors.
- 3.2.1 Scale Regularization: Minimizing each Gaussian’s smallest scaling component flattens the ellipsoid and encourages its center point to align with the surface.
- 3.2.2 Normal Regularization: As a Gaussian flattens, the direction of its minimized scaling factor becomes the normal of the thin Gaussian.
- 3.2.2 Normal Regularization: The Gaussian normal is transformed from camera to world coordinates and aligned with the surface normal predicted by NeuS.
- 3.2.2 Normal Regularization: Normal alignment refines the Gaussian-derived point cloud into a more dependable prior for surface optimization.
3.3. Joint Optimization
NeuSG jointly optimizes NeuS and 3D Gaussian Splatting: NeuS uses Gaussian-derived point constraints, while its predicted normals refine the Gaussians and improve their guidance.
- 3.3.1 Implicit Surface Reconstruction: NeuS is first optimized with rendering, Eikonal, and point-cloud constraints derived from 3D Gaussians.
- 3.3.1 Implicit Surface Reconstruction: The color loss measures discrepancies between input images C and rendered outputs ˆC.
- 3.3.1 Implicit Surface Reconstruction: Eikonal regularization enforces the correct three-dimensional SDF gradient norm, with ∇f(x_i) denoting the SDF gradient at x_i.
- 3.3.1 Implicit Surface Reconstruction: Gaussian-derived points constrain surface optimization by encouraging their predicted SDF values to approach zero.
- 3.3.2 Gaussian Refinement: The Gaussian-splatting loss combines RGB reconstruction with scale minimization and normal alignment, weighted by λ3 and λ4.
- 3.3. Joint Optimization: Mutual optimization refines the Gaussian-derived point cloud, which then provides a more reliable prior.
4. Experiment
NeuSG achieves high-quality surface reconstruction on Tanks and Temples, combining complete geometry with intricate details and efficient training. Ablations and regularization losses support the effectiveness of its Gaussian Splatting guidance and joint optimization.
- 4.1. Comparisons to Baselines: NeuSG achieves the highest reported surface-reconstruction quality on the Tanks and Temples dataset.Table 1 compares NeuSG with neural implicit, geometric-constraint, point-cloud, and classical multi-view stereo methods.
- 4.1. Comparisons to Baselines: NeuSG attains an F1 score of 0.49 versus 0.38 for NeuS, while preserving complete, high-fidelity, intricate surfaces.It also achieves 0.49 versus 0.35 for methods using point clouds as regularization.
- 4.1. Comparisons to Baselines: NeuSG achieves comparable F1 performance to NAngelo-22, 0.49 versus 0.5, with 16 hours of training instead of 128 hours.NAngelo-22 uses 2^22 hash entries per resolution, whereas NeuSG uses the less resource-intensive setting described in the experiment.
- 4.1. Comparisons to Baselines: Qualitative comparisons show complete roofs, separated tires, and detailed stairways and handrails that competing methods miss, merge, or over-smooth.In the Barn scene, NeuSG reconstructs the entire roof without gaps; in the Truck scene, it separates the tire from the ground.
- 4.2.1 Scale and Normal Regularization: Scale and normal regularization losses approach 1×10^-8 and 0.001, respectively, indicating extremely thin Gaussians and accurately learned normals.These losses are reported in Figure 5 as validation of the proposed regularization design.
- 4.2.2 Point Clouds Regularization: Ablations show marginal gains from Vis-MVSNet point clouds, whereas proposed Gaussian Splatting point clouds significantly improve performance; adding scale and normal regularization yields the best result.Without regularization, original Gaussian Splatting point clouds negatively affect quality.
5. Limitation
The approach is constrained by the number and distribution of available multi-view images, making dense viewpoint coverage important for accurate reconstruction.
- Accurate reconstruction depends on dense observations from multiple viewpoints.The method may struggle where images are sparse or unevenly distributed.
- Limited or uneven image coverage can reduce reconstruction accuracy in affected regions.
6. Conclusion
The proposed pipeline combines 3D Gaussian Splatting with neural implicit reconstruction to recover complete, highly detailed surfaces. Thin Gaussian regularization and neural-implicit normal guidance support joint optimization of both representations.
- The pipeline uses 3D Gaussian Splatting to provide dense point clouds with detailed structures for neural implicit reconstruction.
- A scale regularizer makes the 3D Gaussians exceedingly thin, drawing their points closer to the surface.
- Neural implicit surface normals refine the 3D Gaussians instead of relying on a static set of prior points.
- Jointly optimizing 3D Gaussian Splatting and the neural implicit model produces complete surfaces with a high level of detail.