3D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexes (2411.14974v3)

Abstract: Recent advances in radiance field reconstruction, such as 3D Gaussian Splatting (3DGS), have achieved high-quality novel view synthesis and fast rendering by representing scenes with compositions of Gaussian primitives. However, 3D Gaussians present several limitations for scene reconstruction. Accurately capturing hard edges is challenging without significantly increasing the number of Gaussians, creating a large memory footprint. Moreover, they struggle to represent flat surfaces, as they are diffused in space. Without hand-crafted regularizers, they tend to disperse irregularly around the actual surface. To circumvent these issues, we introduce a novel method, named 3D Convex Splatting (3DCS), which leverages 3D smooth convexes as primitives for modeling geometrically-meaningful radiance fields from multi-view images. Smooth convex shapes offer greater flexibility than Gaussians, allowing for a better representation of 3D scenes with hard edges and dense volumes using fewer primitives. Powered by our efficient CUDA-based rasterizer, 3DCS achieves superior performance over 3DGS on benchmarks such as Mip-NeRF360, Tanks and Temples, and Deep Blending. Specifically, our method attains an improvement of up to 0.81 in PSNR and 0.026 in LPIPS compared to 3DGS while maintaining high rendering speeds and reducing the number of required primitives. Our results highlight the potential of 3D Convex Splatting to become the new standard for high-quality scene reconstruction and novel view synthesis. Project page: convexsplatting.github.io.

• The paper introduces 3D Convex Splatting, a novel technique that employs 3D smooth convex primitives to reduce the number of required elements and improve scene accuracy.
• It details a CUDA-optimized rasterization and adaptive convex shape refinement strategy that enhances both rendering efficiency and geometric fidelity.
• The method outperforms 3D Gaussian Splatting by achieving up to 0.81 PSNR and 0.026 LPIPS improvements, indicating strong potential for applications in VR and real-time graphics.

Overview of 3D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexes

The paper "3D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexes" introduces an innovative methodology for rendering radiance fields by leveraging 3D smooth convex primitives. The paper addresses the inherent limitations associated with 3D Gaussian Splatting methods by proposing a more adaptable and computationally efficient approach. This method, termed as 3D Convex Splatting (3DCS), capitalizes on 3D smooth convex shapes to reconstruct dense volumetric scenes with fewer primitives, delivering high-quality novel view synthesis with improved representation of hard edges.

3D Gaussian Splatting (3DGS) has been pivotal in advancing the field of radiance field reconstruction, providing high-quality rendering with enhanced computational efficiency. Despite its capabilities, 3DGS struggles with capturing complex geometries, particularly in scenarios involving hard edges or flat surfaces. This is mainly due to the diffusion of Gaussians in space, leading to a substantial memory footprint and potential inaccuracies in scene representation. The proposed 3D Convex Splatting (3DCS) method significantly mitigates these issues by employing smooth convex shapes, which offer enhanced flexibility for modeling scenes with intricate details and sharp edges.

The methodology hinges on the representation of scenes using 3D smooth convexes, optimized through a CUDA-based rasterizer. This approach allows for efficient rendering while maintaining a lower count of primitives compared to traditional methods. Notably, the results showcase improvements of up to 0.81 PSNR and 0.026 LPIPS over 3DGS in benchmarks such as Mip-NeRF360 and Tanks and Temples. This underscores the method's efficacy in delivering high-fidelity scene reconstructions.

Representation and Optimization Strategy

3D Convex Splatting leverages the concept of smooth convex shapes, as delineated in the paper, to enhance the flexibility and effectiveness of primitive-based representations. The primitive's capabilities extend beyond simple shapes, enabling the modeling of complex volumetric features with a reduced memory footprint. This advancement in representation is coupled with a sophisticated optimization framework, which incorporates smoothness and sharpness parameters to control curvature and diffusion during rendering.

The optimization strategy is further compounded by an adaptive convex shape refinement mechanism, allowing for dynamic enhancements of the primitive set based on the scene's geometric complexity. This enables 3DCS to maintain a balance between computational workload and rendering quality.

Numerical Results and Implications

The results presented in the paper are conclusive and demonstrate the superiority of 3DCS over existing methodologies. It surpasses 3DGS and state-of-the-art neural radiance fields in metrics such as PSNR, LPIPS, and SSIM, while simultaneously reducing the computational demands. This positions 3DCS as a viable alternative for high-quality, real-time novel view synthesis.

The practical implications of this research are substantial, suggesting potential applications across multiple domains, including virtual reality, autonomous navigation, and real-time graphics. Theoretically, the integration of 3D smooth convexes into radiance field rendering frameworks highlights a significant shift towards more geometrically meaningful representations, providing fertile ground for further exploration and refinement in AI-driven graphics rendering.

Future Directions

Speculation on future developments suggests that the adoption of 3DCS could instigate novel avenues in volumetric scene decomposition and modeling. Potential research could explore the integration of machine learning techniques to automate and enhance the primitive-based representation further. Additionally, the method lays the groundwork for developing more compressed and efficient storage solutions for large-scale 3D models, which is critical in contexts requiring bandwidth-efficient storage and transmission.

In conclusion, the paper presents a compelling case for adopting 3D Convex Splatting as a standard in radiance field rendering, promising both theoretical advancement and practical applicability. The attention to rendering fidelity, combined with efficiency gains and the reduction of computational overhead, makes 3DCS an important contribution to the field.