We present billboard Splatting (BBSplat) - a novel approach for 3D scene representation based on textured geometric primitives. BBSplat represents the scene as a set of optimizable textured planar primitives with learnable RGB textures and alpha-maps to control their shape. BBSplat primitives can be used in any Gaussian Splatting pipeline as drop-in replacements for Gaussians. Our method's qualitative and quantitative improvements over 3D and 2D Gaussians are most noticeable when fewer primitives are used, when BBSplat achieves over 1200 FPS. Our novel regularization term encourages textures to have a sparser structure, unlocking an efficient compression that leads to a reduction in storage space of the model. Our experiments show the efficiency of BBSplat on standard datasets of real indoor and outdoor scenes such as Tanks&Temples, DTU, and Mip-NeRF-360. We demonstrate improvements on PSNR, SSIM, and LPIPS metrics compared to the state-of-the-art, especially for the case when fewer primitives are used, which, on the other hand, leads to up to 2 times inference speed improvement for the same rendering quality.
我们提出了 Billboard Splatting (BBSplat)，一种基于纹理几何基元的创新 3D 场景表示方法。BBSplat 将场景表示为一组可优化的纹理平面基元，这些基元具有可学习的 RGB 纹理和 alpha 映射，用于控制其形状。BBSplat 基元可以作为高斯基元的直接替代品，无缝集成到任何 3D Gaussian Splatting (3DGS) 管道中。 与 3D 和 2D 高斯相比，当使用较少的基元时，BBSplat 在定性和定量上的改进最为显著，同时实现了超过 1200 FPS 的渲染速度。我们设计了一种新颖的正则化项，鼓励纹理具有稀疏结构，从而实现高效压缩，大幅减少模型存储空间需求。 在 Tanks&Temples、DTU 和 Mip-NeRF-360 等标准室内外场景数据集上的实验表明，BBSplat 在 PSNR、SSIM 和 LPIPS 等指标上相较当前最先进方法取得了显著改进。尤其是在使用较少基元的情况下，BBSplat 在保持相同渲染质量的同时，实现了高达 2 倍的推理速度提升，展示了其卓越的效率与性能。