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.

本文介绍了一种名为 TRIPS 的新技术，将点云渲染技术与轻量级神经网络相结合，实现了更高的渲染质量和实时帧率，并适用于复杂场景和自动曝光片段。