Point-based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [Kerbl and Kopanas et al. 2023] struggles when tasked with rendering highly detailed scenes, due to blurring and cloudy artifacts. On the other hand, ADOP [R\"uckert et al. 2022] can accommodate crisper images, but the neural reconstruction network decreases performance, it grapples with temporal instability and it is unable to effectively address large gaps in the point cloud. In this paper, we present TRIPS (Trilinear Point Splatting), an approach that combines ideas from both Gaussian Splatting and ADOP. The fundamental concept behind our novel technique involves rasterizing points into a screen-space image pyramid, with the selection of the pyramid layer determined by the projected point size. This approach allows rendering arbitrarily large points using a single trilinear write. A lightweight neural network is then used to reconstruct a hole-free image including detail beyond splat resolution. Importantly, our render pipeline is entirely differentiable, allowing for automatic optimization of both point sizes and positions. Our evaluation demonstrate that TRIPS surpasses existing state-of-the-art methods in terms of rendering quality while maintaining a real-time frame rate of 60 frames per second on readily available hardware. This performance extends to challenging scenarios, such as scenes featuring intricate geometry, expansive landscapes, and auto-exposed footage.

翻译：基于点的辐射场渲染在新视角合成中展现出令人瞩目的结果，巧妙融合了渲染质量与计算效率。然而，该领域的最新方法仍存在不足。3D高斯溅射法（Kerbl与Kopanas等，2023）在处理高细节场景时，因模糊和云状伪影而表现不佳。另一方面，ADOP（Rückert等，2022）能生成更清晰的图像，但神经重建网络降低了性能，存在时间不稳定性，且难以有效处理点云中的较大间隙。本文提出TRIPS（三线性点溅射法），该方法融合了高斯溅射与ADOP的核心理念。我们新技术的基本思路是将点光栅化为屏幕空间图像金字塔，金字塔层级的选择由投影点尺寸决定。通过单次三线性写入即可渲染任意大尺寸的点，随后使用轻量级神经网络重建无孔洞图像，呈现超越溅射分辨率的细节。关键在于，我们的渲染管线完全可微，可自动优化点尺寸与位置。实验表明，TRIPS在渲染质量上超越现有最优方法，同时在常用硬件上保持每秒60帧的实时帧率。该性能可拓展至复杂场景，包括包含复杂几何结构、广阔景观及自动曝光画面等挑战性案例。