Rip-NeRF is a novel method for anti-aliasing radiance fields using ripmap-encoded Platonic solids. The method introduces two key components: Platonic Solid Projection and Ripmap Encoding. The Platonic Solid Projection factorizes 3D space onto the faces of a Platonic solid, enabling the representation of anisotropic 3D areas on distinguishable planes. The Ripmap Encoding uses anisotropic pre-filtering of a learnable feature grid to efficiently and precisely featureize projected anisotropic areas. These components work together to achieve high-fidelity anti-aliasing and efficient rendering. Rip-NeRF is evaluated on both synthetic and real-world datasets, demonstrating state-of-the-art rendering quality, particularly in fine details of repetitive structures and textures. It maintains relatively fast training times and efficient memory usage. The method is effective in handling anisotropic areas that are challenging for traditional methods, such as those induced by cone casting. Rip-NeRF also provides a flexible trade-off between rendering quality and efficiency by selecting different Platonic solids with varying numbers of faces. The method is implemented with a hybrid representation, combining explicit and implicit data structures, and is optimized end-to-end with a photometric loss. The results show that Rip-NeRF outperforms existing methods in terms of rendering quality and efficiency, particularly in anti-aliasing and preserving fine details. The method is also effective in real-world captures, demonstrating its practicality and robustness.Rip-NeRF is a novel method for anti-aliasing radiance fields using ripmap-encoded Platonic solids. The method introduces two key components: Platonic Solid Projection and Ripmap Encoding. The Platonic Solid Projection factorizes 3D space onto the faces of a Platonic solid, enabling the representation of anisotropic 3D areas on distinguishable planes. The Ripmap Encoding uses anisotropic pre-filtering of a learnable feature grid to efficiently and precisely featureize projected anisotropic areas. These components work together to achieve high-fidelity anti-aliasing and efficient rendering. Rip-NeRF is evaluated on both synthetic and real-world datasets, demonstrating state-of-the-art rendering quality, particularly in fine details of repetitive structures and textures. It maintains relatively fast training times and efficient memory usage. The method is effective in handling anisotropic areas that are challenging for traditional methods, such as those induced by cone casting. Rip-NeRF also provides a flexible trade-off between rendering quality and efficiency by selecting different Platonic solids with varying numbers of faces. The method is implemented with a hybrid representation, combining explicit and implicit data structures, and is optimized end-to-end with a photometric loss. The results show that Rip-NeRF outperforms existing methods in terms of rendering quality and efficiency, particularly in anti-aliasing and preserving fine details. The method is also effective in real-world captures, demonstrating its practicality and robustness.