Mip-Splatting: Alias-free 3D Gaussian Splatting (2311.16493v1)

Abstract: Recently, 3D Gaussian Splatting has demonstrated impressive novel view synthesis results, reaching high fidelity and efficiency. However, strong artifacts can be observed when changing the sampling rate, \eg, by changing focal length or camera distance. We find that the source for this phenomenon can be attributed to the lack of 3D frequency constraints and the usage of a 2D dilation filter. To address this problem, we introduce a 3D smoothing filter which constrains the size of the 3D Gaussian primitives based on the maximal sampling frequency induced by the input views, eliminating high-frequency artifacts when zooming in. Moreover, replacing 2D dilation with a 2D Mip filter, which simulates a 2D box filter, effectively mitigates aliasing and dilation issues. Our evaluation, including scenarios such a training on single-scale images and testing on multiple scales, validates the effectiveness of our approach.

• The paper introduces Mip-Splatting, which integrates a 3D smoothing filter and a 2D Mip filter to enforce frequency constraints and reduce high-frequency artifacts.
• The methodology applies Nyquist-based regularization and mimics Mipmapping to adapt rendering quality over varying sampling rates.
• Evaluations on synthetic and real-world datasets demonstrate improved image fidelity and stability without compromising real-time performance.

An Overview of "Mip-Splatting: Alias-free 3D Gaussian Splatting"

The paper under analysis, "Mip-Splatting: Alias-free 3D Gaussian Splatting," presents advancements in the field of 3D Gaussian Splatting (3DGS) aimed at mitigating aliasing and high-frequency artifacts in novel view synthesis (NVS). The authors introduce a technique termed Mip-Splatting, which integrates two distinct filters—a 3D smoothing filter and a 2D Mip filter—into the 3DGS framework, resulting in enhanced rendering quality under various sampling conditions.

Context and Background

Novel view synthesis has garnered significant attention in computer graphics and computer vision, especially with the introduction of Neural Radiance Fields (NeRF). NeRF's effectiveness is primarily attributed to its utilization of multilayer perceptrons (MLPs) for representing scene geometry and appearance. However, recent advancements, such as 3D Gaussian Splatting, offer a compelling alternative by providing real-time rendering at high resolutions while using an explicit representation of scenes.

3DGS represents scenes as sets of 3D Gaussians, rendering them with alpha blending in screen space. Despite its efficiency and integration potential with GPUs, 3DGS experiences artifacts when the sampling rate changes due to camera adjustments. Specifically, issues arise from a lack of 3D frequency constraints and the use of a 2D dilation filter, leading to undesirable dilation and erosion effects in the rendered images.

Key Contributions

In addressing these challenges, Mip-Splatting introduces two major modifications to the original 3DGS framework:

1. 3D Smoothing Filter: This filter serves to regularize the frequency of the 3D representation via a Gaussian low-pass filter applied to each Gaussian primitive. By calculating the maximal sampling frequency from the training views, this approach ensures the 3D Gaussians adhere to the Nyquist limit, effectively reducing high-frequency artifacts during zoom-in operations.
2. 2D Mip Filter: The paper also replaces the 2D dilation filter with a 2D Mip filter, which approximates the box filter used in physical imaging processes. This mimics the Mipmap technique, mitigating aliasing by adapting to the sampling rates during zoom-out operations.

Evaluation and Results

The effectiveness of Mip-Splatting is validated through extensive experiments on synthetic benchmarks like the Blender dataset and the real-world Mip-NeRF 360 dataset. The approach consistently outperforms existing state-of-the-art methods, including Mip-NeRF and other variants, across different scales of training and testing scenarios. At both high resolutions, indicative of zoom-in scenarios, and lower resolutions for zoom-out simulations, Mip-Splatting demonstrates significant improvements in image fidelity and stability. It achieves this without notable performance costs during rendering, preserving the real-time capabilities of 3DGS.

Implications and Future Directions

The introduction of Mip-Splatting marks an essential advancement in 3DGS technology by addressing frequency-related artifacts and adapting to variable sampling conditions. Practically, this enhances the applicability of NVS in various fields, such as virtual reality, robotics, and cinematography, where different viewing conditions and fast rendering speeds are critical.

Theoretically, Mip-Splatting contributes significantly to understanding how frequency constraints and appropriate filtering in both spatial and temporal dimensions can lead to more robust 3D graphics rendering frameworks. Future work might explore further optimizations of the algorithm for specific hardware architectures, explore larger and more diverse datasets for validation, and integrate Mip-Splatting with dynamic scene rendering to see how it performs under rapid changes in scene geometry and lighting. As Mip-Splatting integrates seamlessly into GPU pipelines, it could also spearhead developments in real-time NVS applications beyond static scenes, potentially incorporating elements such as dynamic lighting and motion blur adjustments.