AnySplat：基于无约束视角的前馈式3D高斯溅射

摘要

我们推出了AnySplat，一种用于从未校准图像集合中合成新视角的前馈网络。不同于传统神经渲染流程要求已知相机姿态及针对每个场景进行优化，或近期前馈方法因密集视图计算负担而受限，我们的模型一次性预测所有信息。仅需一次前向传播，即可生成一组3D高斯基元，既编码场景几何与外观，又为每张输入图像提供相应的相机内参和外参。这一统一设计轻松适应于无姿态标注、随意采集的多视角数据集。在广泛的零样本评估中，AnySplat在稀疏与密集视图场景下均达到姿态感知基线的质量，同时超越现有无姿态方法。此外，相比基于优化的神经场，它大幅降低了渲染延迟，使得无约束拍摄环境下的实时新视角合成成为可能。项目页面：https://city-super.github.io/anysplat/

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/

AnySplat：基于无约束视角的前馈式3D高斯溅射

AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views

摘要

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