BillBoard Splatting (BBSplat): Learnable Textured Primitives for Novel View Synthesis (2411.08508v4)

Abstract: We present billboard Splatting (BBSplat) - a novel approach for novel view synthesis 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. The proposed primitives close the rendering quality gap between 2D and 3D Gaussian Splatting (GS), enabling the accurate extraction of 3D mesh as in the 2DGS framework. Additionally, the explicit nature of planar primitives enables the use of the ray-tracing effects in rasterization. Our novel regularization term encourages textures to have a sparser structure, enabling an efficient compression that leads to a reduction in the storage space of the model up to x17 times compared to 3DGS. 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. Namely, we achieve a state-of-the-art PSNR of 29.72 for DTU at Full HD resolution.

• The paper introduces BBSplat, a novel approach that replaces Gaussian primitives with learnable billboard-like textured primitives to enhance rendering quality and efficiency.
• It leverages a CUDA implementation and dictionary-based compression to achieve up to 1200 FPS while maintaining superior perceptual scores.
• The method employs sparse regularization for textures, reducing storage needs and showing promise for real-time applications in resource-constrained environments.

Overview of BillBoard Splatting (BBSplat): Learnable Textured Primitives for Efficient Novel View Synthesis

In this paper, the authors propose a novel approach named BillBoard Splatting (BBSplat) for 3D scene representation aimed at improving the efficiency and quality of novel view synthesis (NVS). The BBSplat method utilizes textured geometric primitives—specifically optimizable textured planar primitives with learnable RGB textures and alpha-maps—to enhance the representation of scenes in NVS tasks. These primitives are designed to replace Gaussians in Gaussian Splatting pipelines, offering improvements in both perceptual similarity scores and inference speed, notably when fewer primitives are employed.

Key Contributions and Methodology

1. Textured Primitives for NVS:
   • BBSplat introduces billboard-like primitives, inspired by traditional billboard techniques used in graphics for model simplification, but with enhanced learnability. These are planar, textured primitives that can efficiently model planar surfaces in a scene, such as backgrounds or walls, which are typically resource-intensive to render with Gaussian primitives.
2. Improved Efficiency and Quality:
   • The method demonstrates qualitative and quantitative improvements over both 2D and 3D Gaussian Splatting techniques. Notably, BBSplat shows a significant performance advantage in scenarios utilizing fewer primitives, achieving up to 1200 FPS without compromising on the quality of rendering.
3. Novel Regularization for Textures:
   • A key component of the method is a novel regularization term encouraging the RGB textures to adopt a sparsely structured format. This sparse arrangement facilitates efficient compression, substantially reducing the storage requirements for these models.
4. CUDA Implementation and Dictionary-Based Compression:
   • The authors implemented efficient texture sampling and back-propagation in CUDA, mitigating additional computation overhead typically associated with textured rendering. Furthermore, they employ dictionary-based compression algorithms to manage the textures' memory footprint effectively.

Experimental Validation and Results

Extensive experiments are carried out on renowned datasets including Tanks{content}Temples, DTU, and Mip-NeRF-360. These experiments substantiate the claim that BBSplat achieves superior results in terms of rendering speed and quality metrics—PSNR, SSIM, and LPIPS—when compared with state-of-the-art methods. Specifically, for similar rendering quality, BBSplat can double inference speed while maintaining a reduced number of primitives.

Discussion of Implications

The potential implications of the BBSplat methodology are twofold. Practically, the ability to perform real-time high-quality rendering with fewer resources opens avenues for deployment in computationally constrained environments such as virtual reality applications and mobile platforms. Theoretically, it underscores the significance of leveraging classic graphical simplifications with modern learnable textures in neural rendering contexts, potentially spurring further research into hybrid approaches that combine traditional graphics methodologies with deep learning-based neural renderers.

Future Directions

While the paper establishes a strong case for the utility and efficiency of BBSplat, it acknowledges limitations in terms of storage requirements and training overhead. Future research could explore advanced compression algorithms to further reduce the footprint of stored textures and optimization strategies to expedite the training process. Additionally, broadening the scope to dynamic scenes and objects with complex motion would greatly enhance the practical applicability of textured primitive methods in NVS.

In conclusion, BBSplat represents an advancement in learnable scene representation by efficiently capitalizing on textured primitives and provides a promising foundation for future research and applications in efficient neural rendering technologies.