Abstract
World building with 3D scene representations is increasingly important for content creation, simulation, and interactive experiences, yet real workflows are inherently iterative: creators must repeatedly extend an existing scene under user control. Motivated by this research gap, we study 3D scene expansion in a user-centric workflow: starting from a real scene captured by multi-view images, we extend its coverage by inserting an additional view synthesized by a generative model. Unlike simple object editing or style transfer in a fixed scene, the inserted view is often 3D-misaligned with the original reconstruction, introducing geometry shifts, hallucinated content, or view-dependent artifacts that break global multi-view consistency. To address the challenge, we propose SceneExpander, which applies test-time adaptation to a parametric feed-forward 3D reconstruction model with two complementary distillation signals: anchor distillation stabilizes the original scene by distilling geometric cues from the captured views, while inserted-view self-distillation preserves observation-supported predictions yet adapts latent geometry and appearance to accommodate the misaligned inserted view. Experiments on ETH scenes and online data demonstrate improved expansion behavior and reconstruction quality under misalignment.
Abstract (translated)
基于3D场景表征的世界构建对于内容创作、仿真和交互体验愈发重要，但实际工作流本质上是迭代的：创作者必须在用户控制下反复扩展现有场景。受此研究空白驱动，我们在以用户为中心的工作流中研究3D场景扩展：从多视角图像捕捉的真实场景出发，通过插入由生成模型合成的额外视图来扩展其覆盖范围。与固定场景中的简单对象编辑或风格迁移不同，插入的视图通常与原始重建存在3D错位，引入几何偏移、幻觉内容或视角相关伪影，从而破坏全局多视角一致性。为应对这一挑战，我们提出SceneExpander，该方法对参数化前馈3D重建模型应用测试时适应，并利用两种互补的蒸馏信号：锚点蒸馏通过从捕捉的视图中蒸馏几何线索来稳定原始场景，而插入视图自蒸馏则保留有观测支持的预测，同时适应潜在几何和外观以容纳错位的插入视图。在ETH场景和在线数据上的实验表明，该方法在错位条件下改善了扩展行为和重建质量。
URL
https://arxiv.org/abs/2603.27084
