IRIS：隐式渲染对无位姿新视角合成的重要性
IRIS: Implicit Rendering Matters for Pose-Free Novel View Synthesis
- National University of Defense Technology（国防科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
IRIS提出一种完全自监督框架，通过潜在神经场和自预测相机实现无位姿新视角合成，兼顾隐式建模的灵活性与显式几何的稳定性，取得高质量渲染和竞争性位姿精度。
AI中文摘要：
从无位姿的多视角图像进行新视角合成仍然具有挑战性，因为模型必须在没有位姿监督的情况下联合学习场景表示和相机参数。现有方法大致分为两个极端：隐式潜在空间渲染灵活且易于优化，但通常导致相机估计的几何基础薄弱；显式3D表示提供更强的几何基础，但引入更重的参数化和更脆弱的优化。在本文中，我们提出了IRIS，一个完全自监督的框架，在这两种范式之间提供了一个实用的中间地带。IRIS不是解码自由潜在标记或重建完全显式的3D基元，而是将场景表示为潜在神经场，并通过在自预测相机下查询该场来渲染新视角。具体来说，参考视图的投影特征在采样的3D点处聚合，形成逐点潜在特征，然后沿目标射线组合以进行渲染。这种设计保留了隐式建模的灵活性和优化稳定性，同时引入了比无约束潜在渲染更强的几何结构。大量实验表明，IRIS在完全自监督学习下实现了强的新视角合成质量和有竞争力的位姿精度。我们的项目页面：此https URL
英文摘要：
Novel view synthesis from unposed multi-view images remains challenging, as the model must jointly learn scene representations and camera parameters without pose supervision. Existing approaches largely fall into two extremes: implicit latent-space rendering is flexible and easy to optimize, but often yields weakly grounded camera estimation; explicit 3D representations provide stronger geometric grounding, but introduce heavier parameterization and more fragile optimization. In this paper, we present IRIS, a fully self-supervised framework that provides a practical middle ground between these two paradigms. Instead of decoding free latent tokens or reconstructing fully explicit 3D primitives, IRIS represents the scene as a latent neural field and renders novel views by querying this field under self-predicted cameras. Specifically, projected features from reference views are aggregated at sampled 3D points to form point-wise latent features, which are then composed along target rays for rendering. This design preserves the flexibility and optimization stability of implicit modeling, while introducing stronger geometric structure than unconstrained latent rendering. Extensive experiments show that IRIS achieves strong novel view synthesis quality with competitive pose accuracy under fully self-supervised learning. Our project page: https://leo-frank.github.io/IRIS
