Introducing RenderFormer: Transformer-Based Neural Rendering of Triangle Meshes with Global Illumination In the world of computer graphics, achieving realistic images from 3D models has long been a complex and computationally intensive process. Traditional methods often rely on ray tracing and rasterization, which can be time-consuming and resource-heavy. However, researchers have developed a novel approach called RenderFormer, a neural rendering pipeline that can generate high-quality images from triangle-based representations of scenes, incorporating full global illumination effects, without the need for per-scene training or fine-tuning. End-to-End Mesh to Image Transformation RenderFormer departs from conventional physics-centric rendering techniques. Instead, it formulates the rendering process as a sequence-to-sequence transformation. The system takes a sequence of tokens, each representing a triangle with specific reflectance properties, and converts it into a sequence of output tokens, which correspond to small patches of pixels in the final image. This approach streamlines the rendering process, making it more efficient and versatile. Simple Transformer Architecture with Minimal Prior Constraints The RenderFormer pipeline consists of two main stages: a view-independent stage and a view-dependent stage. The view-independent stage focuses on modeling the light transport between triangles. It computes how light interacts with each surface in the scene, ensuring that global illumination effects such as shadows, reflections, and indirect lighting are accurately captured. This stage uses a transformer architecture, which allows it to handle large scenes and intricate light paths efficiently. The view-dependent stage then takes the light transport information from the view-independent stage and transforms a token representing a bundle of rays into the corresponding pixel values. This stage also employs a transformer architecture and is designed to produce detailed, accurate images regardless of the camera's perspective. By leveraging both stages, RenderFormer avoids the need for rasterization or ray tracing, significantly reducing computational requirements. Both stages of RenderFormer are learned with minimal prior constraints. This means that the system can adapt to a wide range of scenes and lighting conditions without requiring extensive customization or per-scene tuning, making it highly flexible and user-friendly. Demonstrating Versatility and Quality RenderFormer has been tested on various scenes, showcasing its ability to handle different lighting conditions, materials, and geometric complexities. These examples highlight the system's robustness and effectiveness. Whether the scene features simple shapes with uniform lighting or complex geometries with intricate shadows and reflections, RenderFormer consistently produces high-quality images. For a more comprehensive look at the capabilities of RenderFormer, check out the rendering gallery, which provides detailed reference images. These images demonstrate the system's ability to realistically simulate global illumination effects, including soft shadows, glossy surfaces, and diffuse inter-reflections, all without the need for individual scene training. Overall, RenderFormer represents a significant advancement in neural rendering, offering a streamlined, efficient, and flexible solution for generating realistic images from 3D triangle meshes. Its ability to bypass traditional rendering methods while maintaining high-quality outputs opens up new possibilities for real-time applications and interactive content creation in the field of computer graphics.