RenderFormer: A Transformer-Based Pipeline for Neural Rendering of Triangle Meshes with Full Global Illumination

Transformer-Based Neural Rendering of Triangle Meshes with Full Global Illumination Introduction We introduce RenderFormer, a groundbreaking neural rendering pipeline that can produce highly realistic images from a triangle-based representation of a scene, incorporating full global illumination effects. Unlike traditional rendering techniques, RenderFormer does not require per-scene training or fine-tuning, making it versatile and efficient. End-to-End Mesh to Image Rendering Traditional rendering methods often rely on physics-centric approaches, which involve complex and time-consuming processes such as ray tracing and rasterization. RenderFormer, however, takes a different path. It formulates rendering as a sequence-to-sequence transformation, where a sequence of tokens representing triangles with their reflectance properties is converted into a sequence of output tokens representing small patches of pixels. This innovative approach bypasses the need for explicit geometric processing and illumination calculations, streamlining the rendering process significantly. Simple Transformer Architecture with Minimal Prior Constraints RenderFormer operates using a two-stage pipeline. The first stage, which is view-independent, focuses on modeling the light transport between triangles. The second stage, view-dependent, transforms a token that represents a bundle of rays into the corresponding pixel values, guided by the triangle-sequence generated in the first stage. Both stages leverage the transformer architecture, known for its effectiveness in handling sequential data, and are designed with minimal prior constraints. This allows the model to learn and adapt more flexibly to different scenes and lighting conditions, without the need for per-scene customization. No Rasterization, No Ray Tracing One of the most significant advantages of RenderFormer is that it eliminates the need for rasterization and ray tracing. These traditional techniques, while powerful, can be computationally intensive and limit real-time applications. By avoiding these steps, RenderFormer achieves faster rendering times and greater efficiency, making it particularly suitable for scenarios where rapid image generation is crucial. Rendering Gallery The capabilities of RenderFormer are best illustrated through its rendering gallery. This collection showcases a variety of scenes under different lighting conditions, featuring diverse materials and geometric complexities. Each image demonstrates the model's ability to handle intricate lighting effects and detailed textures without any per-scene training or fine-tuning. For a more comprehensive understanding, refer to the reference images provided, which offer a closer look at the visual outputs and the underlying processes. In summary, RenderFormer represents a significant leap in neural rendering technology, offering a simple yet powerful solution for generating realistic images with full global illumination effects. Its innovative architecture and minimal prior constraints make it a flexible tool for a wide range of applications, from real-time graphics to high-fidelity simulations.