Bridging neural rendering and classical computer graphics

M. Jan Held will publicly defend his thesis entitled "Bridging neural rendering and classical computer graphics".

Summary

Recent advances in neural rendering have improved both the quality and efficiency of novel view synthesis from multi-view images. In particular, 3D Gaussian Splatting stands out by representing scenes with volumetric primitives and yielding high visual fidelity in real time. Despite these successes, point-based primitives struggle with sharp geometry, irregular surfaces, and integration with classical graphics pipelines.

This thesis addresses these limitations by investigating radiance field representations based on alternative geometric primitives. The selection of a particular primitive significantly affects reconstruction accuracy, rendering performance, and compatibility with standard graphics pipelines.
We introduce three complementary approaches that progressively enhance visual quality and integration with graphics pipelines: smooth convexes, triangles, and, finally, culminate in meshes.

First, we introduce 3D Convex Splatting, the first work to move beyond Gaussian primitives by introducing smooth convex shapes. Compared to Gaussians, convex shapes offer greater flexibility and more accurately represent flat surfaces and sharp edges with fewer primitives. Coupled with an efficient CUDA rasterizer, it improves fidelity while reducing memory usage.

Second, we revisit one of the most fundamental primitives in computer graphics: the triangle. The main advantage of triangles is that they are fully compatible with current rendering pipelines. Triangle Splatting develops a differentiable rendering pipeline that enables direct optimization of triangles through end-to-end gradients. It represents a promising first step toward mesh-aware neural rendering, bridging decades of GPU-accelerated graphics with modern differentiable frameworks.

Finally, MeshSplatting extends this line of work by optimizing fully opaque, connected triangles, jointly refining geometry and appearance through differentiable rendering. This produces coherent surfaces that integrate seamlessly with real-time rendering engines, enabling applications such as augmented and virtual reality without any postprocessing.

Together, these contributions show that alternative primitives provide a powerful complement to Gaussian-based radiance fields. By progressing from convex shapes to triangles and finally to meshes, this thesis bridges neural rendering and classical computer graphics, enabling high-quality reconstruction, geometric consistency, efficient rendering, and full compatibility with real-time rendering engines.

Practical information

Defence will take place on Wednesday 13th at 16:00, to all at Amphithéâtre Mania Pavella (Institut Montefiore - B28 - Sart Tilman) or via the FSA PhD Channel.