MixRF: Universal Mixed Radiance Fields With Points and Rays Aggregation

Recent advancements in neural rendering methods, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3D-GS), have significantly revolutionized photo-realistic novel view synthesis of scenes with multiple photos or videos as input. However, existing approaches within the NeRF and 3D-GS frameworks often assume the independence of point sampling and ray casting, which are intrinsic to volume rendering and alpha-blending techniques. These underlying assumptions limit the ability to aggregate context within subspaces, such as densities and colors in the radiance fields and pixels on the image plane, leading to synthesized images that lack fine details and smoothness. To overcome this, we propose a universal framework, MixRF, comprising a Radiance Field Mixer (RF-mixer) and a Color Domain Mixer (CD-mixer), to sufficiently aggregate and fully explore information in neighboring sampled points and casting rays, separately. The RF-mixer treats sampled points as an explicit point cloud, enabling the aggregation of density and color attributes from neighboring points to better capture local geometry and appearance. Meanwhile, the CD-mixer rearranges rendered pixels on the sub-image plane, improving smoothness and recovering fine details and textures. Both mixers employ a kernel-based mixing strategy to facilitate effective and controllable attribute aggregation, ensuring a more comprehensive exploration of radiance values and pixel information. Extensive experiments demonstrate that our MixRF framework is compatible with radiance field-based methods, including NeRF and 3D-GS designs. The proposed framework dramatically enhances performance in both qualitative and quantitative evaluations, with less than a $ 25\%$ increase in computational overhead during inference.