Accurately reconstructing a 3D scene including explicit geometry information
is both attractive and challenging. Geometry reconstruction can benefit from
incorporating differentiable appearance models, such as Neural Radiance Fields
and 3D Gaussian Splatting (3DGS). In this work, we propose a learnable scene
model that incorporates 3DGS with an explicit geometry representation, namely a
mesh. Our model learns the mesh and appearance in an end-to-end manner, where
we bind 3D Gaussians to the mesh faces and perform differentiable rendering of
3DGS to obtain photometric supervision. The model creates an effective
information pathway to supervise the learning of the scene, including the mesh.
Experimental results demonstrate that the learned scene model not only achieves
state-of-the-art rendering quality but also supports manipulation using the
explicit mesh. In addition, our model has a unique advantage in adapting to
scene updates, thanks to the end-to-end learning of both mesh and appearance.

通过将不同可分辨外观模型与显式几何表示相结合，我们提出了一种可学习的场景模型，用于准确重构包含显式几何信息的三维场景，实验结果表明，该模型不仅在渲染质量方面达到了最新水平，而且还支持使用显式网格进行操作，并且具有适应场景更新的独特优势。