將場景視為物體而非基元：來自無姿態視角的實例結構化三維標記化

摘要

三维场景是通过其中的物体来理解的，而非构成物体的图元。然而，前馈重建方法输出的是一组稠密且无结构化的点或高斯分布，物体的结构需要在后续处理中恢复。我们提出了一种前馈框架，该框架直接从无姿态的多视角图像中将场景分解为实例结构化的三维标记组——紧凑的以物体为中心的单元，重建、分割和操作均可由此实现。每一个标记组将一个捕获实体层面身份的实例标记与编码局部几何和外观的锚定标记配对，这些锚定标记随后被解码为一组三维高斯分布。这种双层分解将物体身份与局部外观解耦，使得物体实例成为表示的原生接口，而非派生结果。这些标记组通过可微渲染与联合重建和分割监督进行学习，无需三维标注。我们的前馈模型在类别无关的实例分割任务上超越了逐场景优化的基线方法，同时在新视角合成方面仍具有竞争力。除这些指标外，相同的标记组还直接实现了实例层面的场景编辑——通过操作对应的标记组来移除、平移或插入物体——以及高效的开放词汇三维实例检索，其中检索复杂度随实例数量而非图元数量扩展。

English

A 3D scene is understood through its objects, not the primitives that compose them. Yet feed-forward reconstruction methods output dense, unstructured sets of points or Gaussians, leaving object-level structure to be recovered after the fact. We propose a feed-forward framework that decomposes a scene into instance-structured 3D token groups directly from unposed multi-view images -- compact object-centric units from which reconstruction, segmentation, and manipulation all follow. Each token group pairs an instance token capturing entity-level identity with anchor tokens that encode local geometry and appearance, which are decoded into a set of 3D Gaussians. This two-level factorization decouples object identity from local appearance, making object instances a native interface of the representation rather than a derived product. The token groups are learned through differentiable rendering with joint reconstruction and segmentation supervision, requiring no 3D annotations. Our feed-forward model surpasses per-scene optimization baselines in class-agnostic instance segmentation while remaining competitive in novel view synthesis. Beyond these metrics, the same token groups directly unlock instance-level scene editing -- removing, translating, or inserting objects by operating on their groups -- as well as efficient open-vocabulary 3D instance retrieval, where retrieval complexity scales with the number of instances rather than primitives.