REVIEW 19 cited by
SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian Splatting. Gaussian Splatting has recently become very popular as it yields realistic rendering while being significantly faster to train than NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D gaussians as these gaussians tend to be unorganized after optimization and no method has been proposed so far. Our first key contribution is a regularization term that encourages the gaussians to align well with the surface of the scene. We then introduce a method that exploits this alignment to extract a mesh from the Gaussians using Poisson reconstruction, which is fast, scalable, and preserves details, in contrast to the Marching Cubes algorithm usually applied to extract meshes from Neural SDFs. Finally, we introduce an optional refinement strategy that binds gaussians to the surface of the mesh, and jointly optimizes these Gaussians and the mesh through Gaussian splatting rendering. This enables easy editing, sculpting, rigging, animating, compositing and relighting of the Gaussians using traditional softwares by manipulating the mesh instead of the gaussians themselves. Retrieving such an editable mesh for realistic rendering is done within minutes with our method, compared to hours with the state-of-the-art methods on neural SDFs, while providing a better rendering quality. Our project page is the following: https://anttwo.github.io/sugar/
Forward citations
Cited by 19 Pith papers
-
MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping
MANGO-Grasp uses geometry-oriented 3D Gaussians and Mahalanobis fields to achieve strong cross-embodiment dexterous grasping, with zero-shot transfer to an unseen hand at 84% simulation and 86% real-world success.
-
Points as Tori: Fast Pointwise Signed Distance for Point Clouds
Blending closed-form torus SDFs, with per-point coefficients predicted by a shared neural network, yields pointwise signed distance to point clouds without explicit reconstruction.
-
Beyond a Single Light: A Large-Scale Aerial Dataset for Urban Scene Reconstruction Under Varying Illumination
SkyLume contributes 10 real-world UAV urban regions captured at morning, noon, and afternoon with LiDAR-based ground truth, plus the Temporal Consistency Coefficient metric for cross-time albedo stability.
-
GS-Occ3D: Scaling Vision-only Occupancy Reconstruction with Gaussian Splatting
A camera-only Gaussian-surfel pipeline reconstructs full Waymo scenes, converts them to binary occupancy labels, and trains CVT-Occ to generalize on Occ3D-Waymo and Occ3D-nuScenes at a level close to or above LiDAR-la...
-
Virtual Memory for 3D Gaussian Splatting
A proxy-mesh visibility buffer with page streaming and level of detail lets 3D Gaussian Splatting render scenes larger than GPU memory while culling occluded Gaussians.
-
SGCR: Spherical Gaussians for Efficient 3D Curve Reconstruction
SGCR reconstructs 3D parametric curves from multi-view images by optimizing Spherical Gaussians against 2D edge maps and then fitting rational Bezier curves.
-
FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations
FreBIS replaces VolSDF's single encoder with three frequency-band encoders and a dissimilarity-based weighting module, yielding small rendering-quality gains on 9 BlendedMVS scenes.
-
Flow Distillation Sampling: Regularizing 3D Gaussians with Pre-trained Matching Priors
A new loss that matches an optical-flow model's predictions against analytically computed flows from 3D Gaussians, improving geometric reconstruction on sparse indoor scenes.
-
GaussianPainter: Painting Point Cloud into 3D Gaussians with Normal Guidance
GaussianPainter produces 3D Gaussians from a point cloud and reference image in one forward pass by constraining Gaussian rotations with predicted surface normals.
-
Sensing Surface Patches in Volume Rendering for Inferring Signed Distance Functions
The paper builds small surface patches in the neural SDF field during volume rendering and imposes depth, normal, and photo-consistency losses on them, reporting improved indoor reconstruction.
-
SAGA: Surface-Aligned Gaussian Avatar
A two-stage surface-aligned Gaussian representation (adhere-then-detach) improves monocular human avatar synthesis and enables direct mesh extraction.
-
AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos
A rectangle-with-alpha-channel plane representation with differentiable rasterization gives state-of-the-art 3D planar reconstruction from monocular videos on ScanNet.
-
GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction
GausSurf combines iterative patch-match MVS refinement with 3D Gaussian Splatting and normal priors, achieving state-of-the-art surface reconstruction at 7.2 minutes per DTU scene.
-
FatesGS: Fast and Accurate Sparse-View Surface Reconstruction using Gaussian Splatting with Depth-Feature Consistency
FatesGS combines local monocular depth ranking, depth smoothing, and multi-view feature alignment in a 2D Gaussian splatting pipeline to obtain accurate surface meshes from only three views without dataset-scale pre-training.
-
CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction
CoSurfGS is a distributed device-edge-cloud training framework for 3D Gaussian surface reconstruction that compresses local models and distills them into a global large-scene model, reducing memory and training time.
-
2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains for High-Fidelity Indoor Scene Reconstruction
2DGS-Room guides 2D Gaussian splats with seed points, monocular depth/normal priors, and multi-view consistency, achieving state-of-the-art indoor reconstruction F-scores.
-
ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency
ULSR-GS partitions large aerial scenes by sparse SfM points and trains each sub-region with multi-view geometric consistency, improving surface mesh accuracy.
-
DyGASR: Dynamic Generalized Exponential Splatting with Surface Alignment for Accelerated 3D Mesh Reconstruction
DyGASR reconstructs 3D meshes faster and with less memory by replacing Gaussians with generalized exponential splats, adding SuGaR-style surface alignment, and training at progressively higher resolutions.
-
Enhancing non-Rigid 3D Model Deformations Using Mesh-based Gaussian Splatting
A proposal to combine 3D Gaussian splatting, SAM segmentation, GS2Mesh conversion, LLM-based material assignment, and XPBD physics into a mesh-based editing pipeline, with no experimental validation.
Discussion (0). Continue with ORCID to comment.