ZipSplat：更少的高斯，更優的潑濺

摘要

前馈式3D高斯泼溅方法通过单次前向传播从已知或无姿态图像中重建场景，但现有方法为每个输入像素预测一个高斯点，使得表示预算受限于相机分辨率而非场景复杂度。因此，一面平坦墙壁与一个纹理丰富的物体虽然几何需求差异巨大，却会产生相同数量的高斯点。我们提出ZipSplat——一种基于标记的前馈模型，将高斯点布局与像素网格解耦。多视图骨干网络提取密集视觉标记，并通过k均值聚类将其压缩为紧凑的场景标记集。交叉注意力与自注意力机制精炼这些标记后，轻量级MLP将每个标记解码为一组具有无约束3D位置的高斯点。由于推理时执行聚类，单一训练模型即可覆盖质量-效率曲线而无需重新训练。ZipSplat无需真实姿态或内参即可运行，但在DL3DV和RealEstate10K数据集上以比像素对齐方法少约6倍的高斯点数量创下新最先进水平，分别超越最佳无姿态基线2.1dB和1.2dB PSNR。此外，它零样本泛化至Mip-NeRF360和ScanNet++，优于所有可比基线。项目页面：https://veichta.com/zipsplat。

English

Feed-forward 3D Gaussian Splatting methods reconstruct a scene from posed or pose-free images in a single forward pass, yet current approaches predict one Gaussian per input pixel, tying the representation budget to camera resolution rather than scene complexity. A flat wall and a richly textured object thus produce equally many Gaussians despite very different geometric needs. We propose ZipSplat, a token-based feed-forward model that decouples Gaussian placement from the pixel grid. A multi-view backbone extracts dense visual tokens, and k-means clustering compresses them into a compact set of scene tokens. Cross- and self-attention refine these tokens, and a lightweight MLP decodes each into a group of Gaussians with unconstrained 3D positions. Because clustering is applied at inference, a single trained model spans the quality-efficiency curve without retraining. ZipSplat operates without ground-truth poses or intrinsics, yet sets a new state of the art on DL3DV and RealEstate10K with {sim}6{times} fewer Gaussians than pixel-aligned methods, surpassing the best pose-free baseline by 2.1dB and 1.2dB PSNR, respectively. It further generalizes zero-shot to Mip-NeRF360 and ScanNet++, outperforming all comparable baselines. Our project page is at {https://veichta.com/zipsplat{https://veichta.com/zipsplat}}.