Mip-NeRF 360 is an extension of the neural radiance fields (NeRF) approach designed to handle unbounded scenes, where the camera can point in any direction and content exists at any distance. The original NeRF and its variant, mip-NeRF, face challenges in rendering such scenes due to issues like unbalanced detail, scale, and ambiguity. Mip-NeRF 360 addresses these challenges by introducing a non-linear scene parameterization, online distillation, and a novel distortion-based regularizer. This model achieves a 57% reduction in mean-squared error compared to mip-NeRF and produces realistic synthesized views and detailed depth maps for complex, unbounded real-world scenes. The key challenges addressed include parameterization, efficiency, and ambiguity. Parameterization involves allocating more capacity to nearby content and less to distant content. Efficiency is improved by using a proposal MLP that predicts volumetric density and resamples intervals for the NeRF MLP, reducing training time. Ambiguity is mitigated by a distortion-based regularizer that minimizes the distances between points along the ray, encouraging compact and accurate representations. Mip-NeRF 360 uses a contraction function to map coordinates onto a bounded space, ensuring that distant points are distributed proportionally to disparity. It also employs a linear-in-disparity spacing for ray distances, which aligns with the geometry of perspective projection. The model's architecture includes a proposal MLP and a NeRF MLP, with the proposal MLP trained to produce weights that are consistent with the NeRF MLP's outputs, enabling efficient and accurate rendering. The model outperforms prior NeRF-like methods in terms of PSNR, SSIM, and LPIPS metrics, with a significant reduction in mean-squared error. It also demonstrates superior performance in depth map accuracy and rendering quality, particularly in complex, unbounded scenes. The model's efficiency and accuracy make it suitable for real-world applications, although it requires several hours of training on an accelerator. Despite these limitations, Mip-NeRF 360 represents a significant advancement in handling unbounded scenes with realistic view synthesis.Mip-NeRF 360 is an extension of the neural radiance fields (NeRF) approach designed to handle unbounded scenes, where the camera can point in any direction and content exists at any distance. The original NeRF and its variant, mip-NeRF, face challenges in rendering such scenes due to issues like unbalanced detail, scale, and ambiguity. Mip-NeRF 360 addresses these challenges by introducing a non-linear scene parameterization, online distillation, and a novel distortion-based regularizer. This model achieves a 57% reduction in mean-squared error compared to mip-NeRF and produces realistic synthesized views and detailed depth maps for complex, unbounded real-world scenes. The key challenges addressed include parameterization, efficiency, and ambiguity. Parameterization involves allocating more capacity to nearby content and less to distant content. Efficiency is improved by using a proposal MLP that predicts volumetric density and resamples intervals for the NeRF MLP, reducing training time. Ambiguity is mitigated by a distortion-based regularizer that minimizes the distances between points along the ray, encouraging compact and accurate representations. Mip-NeRF 360 uses a contraction function to map coordinates onto a bounded space, ensuring that distant points are distributed proportionally to disparity. It also employs a linear-in-disparity spacing for ray distances, which aligns with the geometry of perspective projection. The model's architecture includes a proposal MLP and a NeRF MLP, with the proposal MLP trained to produce weights that are consistent with the NeRF MLP's outputs, enabling efficient and accurate rendering. The model outperforms prior NeRF-like methods in terms of PSNR, SSIM, and LPIPS metrics, with a significant reduction in mean-squared error. It also demonstrates superior performance in depth map accuracy and rendering quality, particularly in complex, unbounded scenes. The model's efficiency and accuracy make it suitable for real-world applications, although it requires several hours of training on an accelerator. Despite these limitations, Mip-NeRF 360 represents a significant advancement in handling unbounded scenes with realistic view synthesis.