TopoSurfel: Closing the Loop between Gaussian Surfels and Meshes for Surface Reconstruction

Chuanjin Fan; Wenjie Chang; Bohao Liao; Yujia Chen; Wenfei Yang; Tianzhu Zhang

cs.CVAug 21, 2026

TopoSurfel: Closing the Loop between Gaussian Surfels and Meshes for Surface Reconstruction

Authors: Chuanjin Fan, Wenjie Chang, Bohao Liao, Yujia Chen, Wenfei Yang, Tianzhu Zhang

Organizations: University of Science and Technology of China, Hefei, China · National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory, Hefei, China

Abstract

3D Gaussian Splatting has achieved remarkable success in novel view synthesis. However, extracting high-fidelity surfaces directly from 3DGS remains challenging due to its discrete and unstructured nature. Existing 3DGS-based reconstruction methods typically rely on multi-view geometric consistency or local constraints. Without an explicit structured geometric prior during optimization, these methods often struggle to resolve structural ambiguities, leading to artifacts and floaters, particularly in textureless or occluded regions. To address this limitation, we propose TopoSurfel, a novel framework that closes the loop between Gaussian surfels and continuous meshes. Unlike recent methods that incorporate mesh extraction into the differentiable pipeline by introducing auxiliary neural networks or extra per-Gaussian parameters, we dynamically extract a continuous proxy mesh via a non-trainable differentiable iso-surfacing process. Leveraging this differentiable connection, we introduce a mesh-guided surfel evolution strategy, including normal alignment and geometry-aware density control, to effectively suppress floaters and fill surface holes. Furthermore, to address the initialization challenges in large-scale environments, we propose a spatially aware hybrid re-initialization strategy that ensures robust reconstruction across complex scenes. Extensive experiments demonstrate that TopoSurfel achieves competitive geometric reconstruction accuracy while maintaining high-quality mesh-based novel view synthesis. The code for our method is available at https://github.com/Fan-Treasure/TopoSurfel.

Figures & tables

Figure 1. Overview of our surface reconstruction framework. (a) We propose a synergistic co-evolution scheme that bridges Gaussian surfels and differentiable meshes. By enforcing mutual geometry guidance, our method effectively captures both high-frequency details and coherent surface structures. (b) Reconstruction results on various scenes demonstrate that our method outperforms existing methods in both quality and efficiency. Overview of our surface reconstruction framework.

Figure 2. Overview of the TopoSurfel Pipeline. (Left) Warm-up: We initialize Gaussian surfels from SfM points and obtain an initial mesh via TSDF fusion. (Right) Closed-Loop Optimization: In addition to the surfel training pipeline, we extract a weighted oriented point cloud from surfels, and then reconstruct the Differentiable Proxy Mesh from it using DPSR and DiffMC. Both the proxy mesh and the surfels are rendered to compute geometry consistency losses. Overview of the TopoSurfel Pipeline.

Figure 3. Illustration of Our Mesh-Guided Normal Alignment and Geometry-Aware Density Control Illustration of mesh-guided normal alignment and geometry-aware density control.

CD (mm) $\downarrow$		24	37	40	55	63	65	69	83	97	105	106	110	114	118	122	Avg.	Time
NeRF- Based	VolSDF ( Yariv et al., 2021 )	1.14	1.26	0.81	0.49	1.25	0.70	0.72	1.29	1.18	0.70	0.66	1.08	0.42	0.61	0.55	0.86	$>$ 12h
	NeuS ( Wang et al., 2021 )	0.83	0.98	0.56	0.37	1.13	0.59	0.60	1.45	0.95	0.78	0.52	1.43	0.36	0.45	0.45	0.77	$>$ 12h
	Neuralangelo ( Li et al., 2023 )	0.45	0.74	0.33	0.34	1.05	0.54	0.53	1.33	1.05	0.72	0.43	0.69	0.34	0.38	0.42	0.62	$>$ 12h
Gaussian- Based	SuGaR ( Su et al., 2023 )	1.47	1.33	1.13	0.61	2.25	1.71	1.15	1.63	1.62	1.07	0.79	2.45	0.98	0.88	0.79	1.33	1h
	2DGS ( Huang et al., 2024 )	0.46	0.84	0.31	0.45	0.92	1.01	0.83	1.23	1.30	0.66	0.61	1.07	0.45	0.71	0.54	0.76	11m
	GOF ( Yu et al., 2024b )	0.50	0.82	0.37	0.37	1.12	0.74	0.73	1.18	1.29	0.68	0.77	0.90	0.42	0.66	0.49	0.74	1h

Table 1. Quantitative geometry comparison on the DTU scenes. Our method achieves the lowest Chamfer Distance without monocular depth priors, while maintaining competitive efficiency. denotes the best, second best, third best, respectively.

\dagger

indicates the use of an additional monocular depth prior.

F1-Score $\uparrow$	Geo-NeuS	N-angelo	RaDe-GS	PGSR	QGS	MILo (dense)	Ours
Barn	0.33	0.70	0.43	0.66	0.55	0.64	0.65
Caterpillar	0.26	0.36	0.32	0.41	0.40	0.38	0.43
Courthouse	0.12	0.28	0.21	0.21	0.28	0.31	0.22
Ignatius	0.72	0.89	0.69	0.80	0.81	0.76	0.77
Meetingroom	0.20	0.32	0.25	0.29	0.31	0.28	0.34
Truck	0.45	0.48	0.51	0.60	0.64	0.59	0.64

Table 2. Quantitative geometry comparison on the TNT dataset.

	Indoor scenes			Outdoor scenes
	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
NeRF	26.84	0.790	0.370	21.46	0.458	0.515
Deep Blending	26.40	0.844	0.261	21.54	0.524	0.364
I-NGP	29.15	0.880	0.216	22.90	0.566	0.371
Mip-NeRF 360	31.72	0.917	0.180	24.47	0.691	0.283
3DGS	30.52	0.921	0.199	24.45	0.728	0.240

Table 3. Quantitative comparison of NVS on the Mip-NeRF 360 dataset.

Figure 4. Qualitative comparison of surface reconstruction on the DTU dataset. In heavily occluded regions and areas with few viewpoints, our method recovers more complete geometry and smoother surfaces with fewer artifacts, whereas other methods often produce over-smoothed or noisy surfaces. Qualitative geometry comparison on DTU scenes.

Figure 5. Qualitative results on the TNT dataset. Our method excels in large-scale real-world scenes, producing cleaner surfaces and more accurate structures. While methods like MILo and QGS struggle with holes or floating noise in complex areas, TopoSurfel maintains high geometric fidelity. Qualitative geometry comparison on TNT scenes.

Figure 6. Novel view synthesis results on the Mip-NeRF 360 dataset. TopoSurfel achieves comparable visual quality to existing methods. NVS comparison on mipnerf360 scenes.

	2DGS	GOF	RaDe-GS	PGSR	IMLS-Splat	MILo	Ours
PSNR $\uparrow$	22.10	23.97	23.58	23.14	23.72	23.77	25.23
SSIM $\uparrow$	0.886	0.905	0.903	0.900	0.906	0.910	0.921
LPIPS $\downarrow$	0.106	0.092	0.094	0.095	0.095	0.094	0.087
$\lvert V\rvert$	0.4M	4.1M	3.0M	3.2M	0.3M	1.0M	1.7M

Table 4. Quantitative comparison of mesh-based Novel View Synthesis on the NeRF-Synthetic dataset.

Figure 7. Qualitative comparison of Mesh-based NVS on the NeRF-Synthetic dataset. As shown in the zoomed-in regions, our approach preserves intricate details that are often blurred or lost in other methods. Mesh-based novel view synthesis results on NeRF-Synthetic.

	Training Resources			Rendering	Output Mesh
	#GS (M)	GPU Mem	Time	FPS	#Vertices	Size (MB)
2DGS	0.10	4.1 GB	11m	490	0.4 M	15.32
GOF	0.18	10.1 GB	1h	41	3.8 M	277.1
RaDe-GS	0.13	11.3 GB	14m	436	2.8 M	143.7
PGSR	0.16	6.4 GB	30m	227	3.0 M	152.5
MILo	0.20	9.4 GB	43m	114	0.7M	41

Table 5. Comparison of training resources, rendering performance, and output mesh size across methods, where #GS denotes the number of Gaussians (in millions), GPU Mem denotes peak GPU memory usage (GB), Time denotes training time, FPS denotes frames per second, #Vertices denotes the number of mesh vertices, and Size denotes mesh file size (MB).

Dataset	Grid Resolution	Peak Memory	Time	CD/F1
DTU	$256^{3}$	4.2 GB	27m	0.54
	$512^{3}$	8.5 GB	37m	0.51
	$720^{3}$	17.5 GB	60m	0.50
TNT	$256^{3}$	9.6 GB	76m	0.45
	$512^{3}$	14.7 GB	98m	0.52
	$720^{3}$	OOM	–	–

Table 6. Memory stress test under different DPSR grid resolutions on an RTX 3090 GPU with 24 GB memory. We report peak memory, training time, Chamfer distance on DTU (

\downarrow

), and F1-score on TNT (

\uparrow

Setting	F1-Score $\uparrow$	PSNR $\uparrow$
w/o Mesh Loop	0.42	26.75
w/o Warm-up	0.39	26.46
View-Statistics Normal Flips	0.46	26.91
Random Normal Flips	0.37	26.20
w/o Hybrid Initialization	0.21	20.59
w/o Geo-Aware Density Control	0.48	27.03

Table 7. Ablation study on the TNT dataset.

Figure 8. Ablation study on the warm-up stage. (a) Gaussian point clouds colored by depth and (b) extracted proxy meshes. In each subfigure, the left side shows the result without warm-up, while the right side shows the result with our warm-up initialization. Ablation of the warm-up stage and mesh-in-the-loop optimization.

Figure 9. Ablation study on mesh-guided normal alignment. (a) Gaussian point clouds colored by surfel normals and (b) extracted proxy meshes. For both (a) and (b), we compare the baseline using only view statistics (left) against our full strategy with mesh-guided normal alignment (right). Ablation of mesh-guided normal alignment.

Figure 10. Ablation study on hybrid re-initialization for large-scale scenes. (a) Without hybrid re-initialization. (b) With hybrid re-initialization. From left to right, we show the rendered RGB images, normal maps, and depth maps. Ablation of hybrid re-initialization for large-scale scenes.

Figure 11. Ablation study on geometry-aware density control. (a) Gaussian point clouds colored by depth and (b) final reconstructed meshes. Within each subfigure, the left side uses only gradient-guided density control, whereas the right side incorporates our geometry-aware density control. Ablation of geometry-aware density control.

Appendix figures & tables10 assets

Supplementary material from the paper’s appendix.

Appendix

Optimization hyperparameters
Hyperparameter	Value
position_lr_init	$1.6\times 10^{-4}$
position_lr_final	$1.6\times 10^{-6}$
feature_lr	$2.5\times 10^{-3}$
opacity_lr	$5.0\times 10^{-2}$
scaling_lr	$5.0\times 10^{-3}$

Appendix

Table 8. Hyperparameters used in our implementation.

Mesh extraction
Setting	F1-Score $\uparrow$	PSNR $\uparrow$	Time
DPSR final Extraction	0.43	26.83	98m
FlexiCubes Extraction	0.39	26.67	137m
Sampling and weighting
1-point + opacity	0.49	26.85	92m
1-point + opacity-area	0.51	26.81	96m

Appendix

Table 9. Additional ablation studies on the TNT dataset.

Figure 12. Ablation of final mesh extraction with DPSR and FlexiCubes. (a) Mesh extracted with DPSR. (b) Mesh extracted with FlexiCubes. (c) Mesh extracted with TSDF fusion. Final Mesh Extraction with DPSR and FlexiCubes.

Figure 13. Ablation of normal estimation without Gaussian flattening (a) Mesh extracted with GOF’s normal estimation. (b) Mesh extracted with RaDe-GS’s normal estimation. (c) Mesh extracted with our normal estimation. Normal Estimation without Gaussian Flattening.

Figure 14. Visualization of Our Reconstructed Meshes on the DTU Dataset. Visualization of Our Reconstructed Meshes on the DTU Dataset.

Method	Chair			Drums			Ficus			Hotdog
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
2DGS	25.84	0.915	0.066	20.27	0.881	0.108	17.68	0.876	0.120	26.33	0.933	0.071
GOF	26.50	0.935	0.053	21.54	0.896	0.101	23.63	0.923	0.083	27.32	0.950	0.062
PGSR	26.92	0.935	0.055	20.64	0.888	0.100	22.82	0.905	0.082	27.22	0.953	0.057
IMLS-Splatting	27.10	0.943	0.047	21.53	0.901	0.100	23.64	0.916	0.088	28.18	0.958	0.053
Ours	28.01	0.943	0.056	22.50	0.914	0.085	25.19	0.940	0.068	30.52	0.967	0.051

Appendix

Table 10. Quantitative evaluation of novel view synthesis on each scene of the NeRF-Synthetic dataset.

Figure 15. Visualization of Our Reconstructed Meshes and trained UV map on the NeRF-Synthetic Dataset. Visualization of Our Reconstructed Meshes and trained UV map on the NeRF-Synthetic Dataset.

Figure 16. Evolution of the differentiable proxy mesh during closed-loop optimization. The proxy mesh is used as a training-time geometric prior. Iterations are counted from the beginning of the mesh-guided stage. Evolution of the differentiable proxy mesh during closed-loop optimization.

Figure 17. Failure case on the Materials scene of NeRF-Synthetic. Failure Case.

Figure 18. Comprehensive visualization of results on multiple Mip-NeRF 360 scenes, including ground-truth images, our rendered images, reconstructed meshes, Surfel normals, and mesh normals. Comprehensive visualization of results on multiple Mip-NeRF 360 scenes, showing rendered images, reconstructed meshes, Surfel normals, and mesh normals.

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3D Gaussian Splatting (3DGS) has recently enabled real-time novel view synthesis with impressive quality. However, it struggles to recover accurate surfaces under limited viewpoints and due to the inherent irregularity of Gaussian primitives. The resulting geometric errors are notoriously difficult to correct manually. To address these issues, we propose Gaussian Sculpting, a fully differentiable end-to-end framework for high-quality surface reconstruction. Our key insight is to anchor Gaussians onto an evolving differentiable surface, allowing them to guide signed distance field (SDF) optimization instead of extracting the surface only during post-processing. To enable stable gradient isolation during joint optimization, we design a bi-level training strategy in which the outer loop optimizes the geometry represented by the SDF, while the inner loop updates the Gaussians with the geometry fixed. We further impose constraints on Gaussian parameters to ensure consistency with the underlying surface, thereby improving both geometric and appearance fidelity during optimization. In addition, we introduce a multi-resolution subdivision scheme based on octree-like partitioning to preserve fine details while reducing memory consumption. Experiments on object-level scenes demonstrate that our method effectively removes redundant surfaces, recovers missing structures caused by limited viewpoints, and achieves strong reconstruction quality even at relatively low resolutions.

Abstract

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