无需相机参数，一张图生成3D高斯点云实现快速新视角合成
AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
- 单次前向传播直接预测3D高斯点与相机参数
- 零样本测试下稠密视图质量媲美已知位姿方法
- 适合无标注、随意拍摄的多视角图像集
我们提出 AnySplat，一种从非校准图像集合中进行新视角合成的前馈网络。与传统神经渲染需已知相机位姿并逐场景优化不同，或近期前馈方法在密集视图下计算负担过重，本模型可一次性完成所有预测。单次前向传播输出一组3D高斯原语，编码场景几何与外观，以及每张输入图像对应的相机内参和外参。该统一设计可轻松扩展至未标注、随意拍摄的多视角数据集。在广泛的零样本评估中，AnySplat 在稀疏与稠密视图场景下均达到与已知位姿基线相当的质量，同时超越现有无位姿方法。此外，相比基于优化的神经场，其渲染延迟大幅降低，使非约束采集场景下的实时新视角合成成为可能。
原文摘要 · Abstract (English)
We introduce AnySplat, a feed forward network for novel view synthesis from uncalibrated image collections. In contrast to traditional neural rendering pipelines that demand known camera poses and per scene optimization, or recent feed forward methods that buckle under the computational weight of dense views, our model predicts everything in one shot. A single forward pass yields a set of 3D Gaussian primitives encoding both scene geometry and appearance, and the corresponding camera intrinsics and extrinsics for each input image. This unified design scales effortlessly to casually captured, multi view datasets without any pose annotations. In extensive zero shot evaluations, AnySplat matches the quality of pose aware baselines in both sparse and dense view scenarios while surpassing existing pose free approaches. Moreover, it greatly reduce rendering latency compared to optimization based neural fields, bringing real time novel view synthesis within reach for unconstrained capture settings.Project page: https://city-super.github.io/anysplat/
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