AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radius (2409.08669v1)

Abstract: 3D Gaussian Splatting (3DGS) is a recent explicit 3D representation that has achieved high-quality reconstruction and real-time rendering of complex scenes. However, the rasterization pipeline still suffers from unnecessary overhead resulting from avoidable serial Gaussian culling, and uneven load due to the distinct number of Gaussian to be rendered across pixels, which hinders wider promotion and application of 3DGS. In order to accelerate Gaussian splatting, we propose AdR-Gaussian, which moves part of serial culling in Render stage into the earlier Preprocess stage to enable parallel culling, employing adaptive radius to narrow the rendering pixel range for each Gaussian, and introduces a load balancing method to minimize thread waiting time during the pixel-parallel rendering. Our contributions are threefold, achieving a rendering speed of 310% while maintaining equivalent or even better quality than the state-of-the-art. Firstly, we propose to early cull Gaussian-Tile pairs of low splatting opacity based on an adaptive radius in the Gaussian-parallel Preprocess stage, which reduces the number of affected tile through the Gaussian bounding circle, thus reducing unnecessary overhead and achieving faster rendering speed. Secondly, we further propose early culling based on axis-aligned bounding box for Gaussian splatting, which achieves a more significant reduction in ineffective expenses by accurately calculating the Gaussian size in the 2D directions. Thirdly, we propose a balancing algorithm for pixel thread load, which compresses the information of heavy-load pixels to reduce thread waiting time, and enhance information of light-load pixels to hedge against rendering quality loss. Experiments on three datasets demonstrate that our algorithm can significantly improve the Gaussian Splatting rendering speed.

• The paper introduces an adaptive radius approach that preemptively culls low-opacity Gaussian-tile pairs, significantly reducing serial processing overhead.
• It refines Gaussian size calculations with an axis-aligned bounding box to target culling in 2D directions and eliminate redundant computations.
• A load balancing algorithm is employed to distribute pixel processing uniformly, achieving up to a 310% increase in rendering speed while preserving quality.

Accelerating Gaussian Splatting with Adaptive Radius

In the paper "AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radius," the authors Xinzhe Wang, Ran Yi, and Lizhuang Ma present an advanced technique aimed at enhancing the efficiency of 3D Gaussian Splatting (3DGS), an explicit 3D representation method known for its high-quality, real-time rendering capabilities. This research primarily addresses the computational overhead and load imbalance inherent in the rasterization pipeline, proposing solutions that significantly boost rendering speeds without compromising image quality.

Key Contributions

1. Early Culling with Adaptive Radius: To expedite the rasterization process, the authors propose an innovative method to preemptively cull Gaussian-Tile pairs with low splatting opacity during the Preprocess stage. This parallel approach, based on an adaptive radius, allows part of the serial culling typically done in the Render stage to be moved earlier in the pipeline. This reduces unnecessary overhead associated with the sequential processing of Gaussians in the rendering phase. The radius is derived from the bounding circle of an ellipse constituted by the minimum splatting opacity, ensuring that the number of processed Gaussians aligns more closely with their actual impact on the rendered image. This early culling mechanism is critical in achieving a substantial improvement in rendering speed.
2. Axis-Aligned Bounding Box (AABB) for Gaussian Splatting: Further streamlining the culling process, the introduction of an axis-aligned bounding box (AABB) specifically tailored for Gaussian splatting calculates Gaussian sizes more accurately in 2D directions. This method enables different extents of culling in horizontal and vertical directions, effectively reducing the range of pixels each Gaussian influences. The approach is particularly beneficial in densely populated scenes where the conventional bounding circle may result in redundant computations.
3. Load Balancing for Pixel-Parallel Splatting: Recognizing the inefficiencies caused by uneven distribution of computational loads across pixel threads, the paper introduces a load balancing algorithm. By quantifying the load variance among pixel threads and appropriately adjusting the number of Gaussians rendered, this technique minimizes thread waiting time. The load balancing approach systematically reduces the variance in the number of Gaussians processed by each pixel, optimizing thread utilization and ensuring more uniform computational distribution. This is achieved through a combination of reducing the load on heavily burdened pixels and enhancing the information processed by lighter-load pixels, thus maintaining overall image quality.

Experimental Results

The proposed methods were evaluated across several datasets, including Mip-NeRF360, Tanks and Temples, and Deep Blending. The experimental results demonstrated a remarkable 310% average acceleration in rendering speeds compared to the baseline 3DGS, with minimal or no quality loss in synthesized images. Notably, the rendering speed reached 590 FPS on the Mip-NeRF360 dataset using a single NVIDIA RTX 3090 GPU. Furthermore, the techniques enhanced the training speed and reduced model size, with the load balancing method proving particularly effective in complex, high-frequency information scenes.

Theoretical and Practical Implications

The advancements posited in this paper have significant implications for both theoretical research and practical applications in computer graphics and rendering. Theoretically, the introduction of adaptive radius and AABB for Gaussian splatting enriches the existing methodologies for efficient 3D representation and rendering. This approach paves the way for more refined models that can further optimize the balance between rendering quality and speed. Practically, the substantial speed improvements marked by these methodologies can enhance real-time applications such as virtual reality, autonomous driving, and other domains requiring high-quality, real-time 3D reconstructions. The improved efficiency also enables rendering in resource-constrained environments, expanding the applicability of high-fidelity graphics to a broader range of hardware profiles.

Future Directions

As the field progresses, future research might focus on further integrating and optimizing these methods with other representation techniques, such as neural radiance fields (NeRF). Additionally, exploring adaptive thresholds for minimum splatting opacity could yield even more efficient early culling strategies. Another promising avenue is the application of machine learning techniques to dynamically adjust rendering parameters based on scene complexity in real-time, thereby achieving even finer-grained control over rendering performance versus quality trade-offs.

In conclusion, the contributions of this paper provide a substantial leap in the efficiency of Gaussian splatting, offering robust solutions to longstanding challenges in rendering pipeline optimization. These innovations not only elevate the performance capabilities of 3DGS but also set a new benchmark for future research in the domain.