**Rip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic Solids** **Abstract:** Despite significant advancements in Neural Radiance Fields (NeRFs), aliasing and blurring artifacts remain a fundamental challenge. This paper introduces Rip-NeRF, a novel representation that uses Ripmap-Encoded Platonic Solids to efficiently and precisely characterize 3D anisotropic areas, achieving high-fidelity anti-aliased renderings. The key components are Platonic Solid Projection and Ripmap encoding. Platonic Solid Projection factorizes 3D space onto the faces of a Platonic solid, enabling precise and efficient characterization of anisotropic areas. Ripmap encoding, constructed by anisotropically pre-filtering a learnable feature grid, enables anisotropic area-sampling. Extensive experiments on synthetic and real-world datasets demonstrate that Rip-NeRF achieves state-of-the-art rendering quality while maintaining efficient reconstruction. The method offers a flexible trade-off between rendering quality and efficiency, making it suitable for various applications. **Keywords:** novel view synthesis, radiance fields, anti-aliasing, anisotropic area-sampling **Contributions:** - Proposes Platonic Solid Projection to represent 3D scenes with 2D faces of Platonic solids, enabling precise and efficient characterization of anisotropic areas. - Introduces Ripmap Encoding to enable anisotropic area-sampling for precise featureization of anisotropic 2D and 3D Gaussians. - Achieves state-of-the-art rendering quality on synthetic and real-world datasets while maintaining efficient reconstruction. - Offers a flexible trade-off between rendering quality and efficiency through the selection of different Platonic solids. **Methods:** - **Platonic Solid Projection:** Projects 3D areas onto the faces of a Platonic solid, enabling precise and efficient characterization. - **Ripmap Encoding:** Constructs a learnable feature grid pre-filtered with anisotropic kernels to enable anisotropic area-sampling. **Experiments:** - **Synthetic Datasets:** Rip-NeRF outperforms other methods in terms of PSNR, SSIM, and LPIPS metrics, demonstrating superior rendering quality and efficiency. - **Real-World Datasets:** Rip-NeRF shows superior performance in rendering intricate structures and appearance details compared to competing methods. **Ablation Study:** - Evaluates the effectiveness of individual components and the synergy between them. **Limitations:** - Challenges in unbounded scenes due to non-convex shapes and space warping mechanisms. **Conclusion:** Rip-NeRF effectively addresses the challenge of aliasing in NeRFs by providing a precise and efficient representation of anisotropic areas, achieving high-fidelity anti-aliased renderings.**Rip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic Solids** **Abstract:** Despite significant advancements in Neural Radiance Fields (NeRFs), aliasing and blurring artifacts remain a fundamental challenge. This paper introduces Rip-NeRF, a novel representation that uses Ripmap-Encoded Platonic Solids to efficiently and precisely characterize 3D anisotropic areas, achieving high-fidelity anti-aliased renderings. The key components are Platonic Solid Projection and Ripmap encoding. Platonic Solid Projection factorizes 3D space onto the faces of a Platonic solid, enabling precise and efficient characterization of anisotropic areas. Ripmap encoding, constructed by anisotropically pre-filtering a learnable feature grid, enables anisotropic area-sampling. Extensive experiments on synthetic and real-world datasets demonstrate that Rip-NeRF achieves state-of-the-art rendering quality while maintaining efficient reconstruction. The method offers a flexible trade-off between rendering quality and efficiency, making it suitable for various applications. **Keywords:** novel view synthesis, radiance fields, anti-aliasing, anisotropic area-sampling **Contributions:** - Proposes Platonic Solid Projection to represent 3D scenes with 2D faces of Platonic solids, enabling precise and efficient characterization of anisotropic areas. - Introduces Ripmap Encoding to enable anisotropic area-sampling for precise featureization of anisotropic 2D and 3D Gaussians. - Achieves state-of-the-art rendering quality on synthetic and real-world datasets while maintaining efficient reconstruction. - Offers a flexible trade-off between rendering quality and efficiency through the selection of different Platonic solids. **Methods:** - **Platonic Solid Projection:** Projects 3D areas onto the faces of a Platonic solid, enabling precise and efficient characterization. - **Ripmap Encoding:** Constructs a learnable feature grid pre-filtered with anisotropic kernels to enable anisotropic area-sampling. **Experiments:** - **Synthetic Datasets:** Rip-NeRF outperforms other methods in terms of PSNR, SSIM, and LPIPS metrics, demonstrating superior rendering quality and efficiency. - **Real-World Datasets:** Rip-NeRF shows superior performance in rendering intricate structures and appearance details compared to competing methods. **Ablation Study:** - Evaluates the effectiveness of individual components and the synergy between them. **Limitations:** - Challenges in unbounded scenes due to non-convex shapes and space warping mechanisms. **Conclusion:** Rip-NeRF effectively addresses the challenge of aliasing in NeRFs by providing a precise and efficient representation of anisotropic areas, achieving high-fidelity anti-aliased renderings.