3D Gaussian Splatting (GS) enables fast and high-quality scene reconstruction, but it lacks an object-consistent and semantically aware structure. We propose Split&Splat, a framework for panoptic scene reconstruction using 3DGS. Our approach explicitly models object instances. It first propagates instance masks across views using depth, thus producing view-consistent 2D masks. Each object is then reconstructed independently and merged back into the scene while refining its boundaries. Finally, instance-level semantic descriptors are embedded in the reconstructed objects, supporting various applications, including panoptic segmentation, object retrieval, and 3D editing. Unlike existing methods, Split&Splat tackles the problem by first segmenting the scene and then reconstructing each object individually. This design naturally supports downstream tasks and allows Split&Splat to achieve state-of-the-art performance on the ScanNetv2 segmentation benchmark.

翻译：3D高斯溅射（GS）能够实现快速且高质量的场景重建，但缺乏对象一致且语义感知的结构。我们提出Split&Splat，一个利用3DGS进行全景场景重建的框架。我们的方法显式地建模对象实例。它首先利用深度信息在多个视角间传播实例掩码，从而生成视角一致的二维掩码。随后每个对象被独立重建，并在优化其边界的同时合并回场景中。最后，在重建对象中嵌入实例级语义描述符，以支持包括全景分割、对象检索和三维编辑在内的多种应用。与现有方法不同，Split&Splat通过先分割场景再独立重建每个对象来解决该问题。这一设计天然支持下游任务，并使Split&Splat在ScanNetv2分割基准测试中达到了最先进的性能。