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.
近年来，辐射场重建技术取得了显著进展，例如3D Gaussian Splatting（3DGS），通过使用高斯原语的组合来表示场景，成功实现了高质量的新视图合成和快速渲染。然而，3D高斯在场景重建中存在一些局限性。在不显著增加高斯数量的情况下，很难准确捕捉场景中的硬边，从而导致较大的内存占用。此外，它们难以表示平坦表面，因为高斯分布在空间中较为弥散。如果没有精心设计的正则化器，高斯原语通常会在实际表面周围不规则地分散。为了解决这些问题，我们提出了一种新方法，称为3D Convex Splatting（3DCS），利用3D平滑凸体作为原语，从多视角图像中建模具有几何意义的辐射场。相比高斯原语，平滑凸体具有更大的灵活性，能够以更少的原语更好地表示具有硬边和高密度区域的3D场景。在我们高效的基于CUDA的光栅化器支持下，3DCS在多个基准测试中（如Mip-NeRF360、Tanks and Temples和Deep Blending）表现优于3DGS。具体而言，与3DGS相比，我们的方法在PSNR上提高了多达0.81，在LPIPS上提升了0.026，同时保持了高渲染速度并减少了所需原语的数量。我们的结果凸显了3D Convex Splatting在高质量场景重建和新视图合成领域成为新标准的潜力。