离网：面向前馈三维高斯泼溅的图元检测
Off The Grid: Detection of Primitives for Feed-Forward 3D Gaussian Splatting
将关键点检测思想引入前馈3DGS的图元放置，用子像素级自适应分布替代像素网格，并基于香农熵动态分配每个图像块的图元数量，为提升前馈三维重建的质量与效率提供了可复用的解码器设计。
原文依据 detects 3D Gaussian primitives at a sub-pixel level, replacing the pixel grid with an adaptive, "Off-The-Grid" distribution
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论前馈三维高斯泼溅（3DGS）模型能够实现实时场景生成，但受限于次优的像素对齐图元放置方式，其依赖密集且刚性的网格，限制了生成质量与效率。我们提出一种新的前馈架构，在子像素级别检测三维高斯基元，用自适应的“离网”分布替代像素网格。受关键点检测启发，我们的解码器学习在图像块之间局部地分布图元。我们还提供一种自适应密度机制，根据香农熵为每个图像块分配不同数量的图元。我们将所提出的解码器与预训练的三维重建骨干网络结合，并在没有任何三维标注的情况下使用光度监督进行端到端训练。所得的无位姿模型能在数秒内生成逼真的三维GS场景，在前馈模型中实现了最优的新视角合成性能。它在使用远少图元的情况下优于竞争对手，展示了更准确且高效的分配，能够捕捉细节并减少伪影。
英文原文摘要 1109 字符
Feed-forward 3D Gaussian Splatting (3DGS) models enable real-time scene generation but are hindered by suboptimal pixel-aligned primitive placement, which relies on a dense, rigid grid that limits both quality and efficiency. We introduce a new feed-forward architecture that detects 3D Gaussian primitives at a sub-pixel level, replacing the pixel grid with an adaptive, "Off-The-Grid" distribution. Inspired by keypoint detection, our decoder learns to locally distribute primitives across image patches. We also provide an Adaptive Density mechanism by assigning varying number of primitives per patch based on Shannon entropy. We combine the proposed decoder with a pre-trained 3D reconstruction backbone and train them end-to-end using photometric supervision without any 3D annotation. The resulting pose-free model generates photorealistic 3DGS scenes in seconds, achieving state-of-the-art novel view synthesis for feed-forward models. It outperforms competitors while using far fewer primitives, demonstrating a more accurate and efficient allocation that captures fine details and reduces artifacts.