Emergent Mind


The Gaussian splatting for radiance field rendering method has recently emerged as an efficient approach for accurate scene representation. It optimizes the location, size, color, and shape of a cloud of 3D Gaussian elements to visually match, after projection, or splatting, a set of given images taken from various viewing directions. And yet, despite the proximity of Gaussian elements to the shape boundaries, direct surface reconstruction of objects in the scene is a challenge. We propose a novel approach for surface reconstruction from Gaussian splatting models. Rather than relying on the Gaussian elements' locations as a prior for surface reconstruction, we leverage the superior novel-view synthesis capabilities of 3DGS. To that end, we use the Gaussian splatting model to render pairs of stereo-calibrated novel views from which we extract depth profiles using a stereo matching method. We then combine the extracted RGB-D images into a geometrically consistent surface. The resulting reconstruction is more accurate and shows finer details when compared to other methods for surface reconstruction from Gaussian splatting models, while requiring significantly less compute time compared to other surface reconstruction methods. We performed extensive testing of the proposed method on in-the-wild scenes, taken by a smartphone, showcasing its superior reconstruction abilities. Additionally, we tested the proposed method on the Tanks and Temples benchmark, and it has surpassed the current leading method for surface reconstruction from Gaussian splatting models. Project page: https://gs2mesh.github.io/.
Pipeline showing steps from 3DGS model scene representation to integrated RGB-D structure using TSDF.


  • A new method for surface reconstruction from Gaussian splatting models utilizing novel-view synthesis for stereo image creation and depth information extraction is introduced.

  • This approach avoids direct spatial alignment of Gaussian elements, instead generating stereo image pairs, applying stereo matching for depth extraction, and integrating depth data for a unified surface.

  • The method has been empirically validated across various scenes, showing superior fidelity and detail in reconstruction over current methods with reduced computational demand.

  • Future directions include improving the initial 3DGS capture process and the stereo matching algorithms, particularly for challenging surfaces like transparency.


Researchers have proposed a novel method for surface reconstruction from Gaussian splatting models by exploiting the capacity for high-quality novel-view synthesis inherent in such models. Unlike existing strategies that attempt to align Gaussian elements spatially to build a surface directly, this approach synthesizes stereo image pairs from novel viewpoints and extracts depth information via stereo matching. This depth data, integrated across views, forms a geometrically consistent surface representation. This methodology not only improves reconstruction fidelity and detail over current methods but does so with significantly reduced computational demand.

Surface Reconstruction Challenge in Gaussian Splatting

Gaussian splatting models (3DGS) optimize a cloud of 3D Gaussians to match input images across varying viewpoints. Despite their effectiveness in novel viewpoint synthesis, leveraging the spatial information of Gaussian elements for direct surface reconstruction presents substantial challenges. The disconnection between the Gaussian elements' optimized positions for image matching and their utility in forming a coherent surface structure leads to inaccurate and noisy reconstructions.

Our Novel Approach

The proposed methodology introduces an innovative pipeline that leverages 3DGS's novel-view synthesis in stereo calibration form to generate depth maps, which are then combined to form a refined, geometrically consistent surface mesh. This process involves:

  • Capturing scenes using 3DGS to enable the generation of synthetic stereo image pairs.

  • Applying stereo matching algorithms to these image pairs to derive depth information.

  • Integrating collected depth data using Truncated Signed Distance Function (TSDF) techniques to produce a unified and smooth surface mesh.

Additionally, the paper describes a semi-automatic approach for extracting specific objects within scenes for targeted reconstruction by combining depth maps with segmentation masks.

Empirical Validation

The effectiveness of the proposed method is confirmed through extensive testing across various scenes, including those captured in uncontrolled, "in-the-wild" conditions using standard smartphone cameras. Results on the Tanks and Temples benchmark further validate its superiority in surface reconstruction from Gaussian splatting models over the current leading methods. Notably, the method achieves detailed and accurate reconstructions comparable to the best neural surface reconstruction techniques but with a fraction of the computational expense.

Limitations and Future Directions

While the method marks a significant advancement, it is not without limitations. The reconstruction's fidelity relies heavily on the initial 3DGS scene capture quality. Inaccuracies in the Gaussian splatting model, particularly in less well-defined regions, can propagate errors into the final surface reconstruction. Additionally, the stereo matching process might struggle with transparent surfaces, potentially leading to incomplete or inaccurate reconstructions in these areas.

Future work could explore improving the robustness of the initial 3DGS capture process and enhancing stereo matching algorithms to better handle challenging surface properties like transparency. The fusion of depth data could also benefit from advances in integrating diverse information sources, potentially leading to even more accurate and detailed surface reconstructions.


This research introduces a significant methodological shift in surface reconstruction from Gaussian splatting models, focusing on leveraging novel-view synthesis for depth extraction rather than direct geometric manipulation of Gaussian elements. This strategy not only surpasses existing methods in accuracy and detail but also significantly reduces computational requirements, representing a meaningful advancement in the field of three-dimensional scene reconstruction.

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