标题： InstaScene：从杂乱场景中实现完整的3D实例分解和重建 显示英文标题

标题： InstaScene: Towards Complete 3D Instance Decomposition and Reconstruction from Cluttered Scenes

摘要： 人类可以自然地识别并 mentally 完成杂乱环境中的遮挡物体。 然而，即使使用先进的重建技术，赋予机器人类似的认知能力仍然具有挑战性，这些技术将场景建模为未区分的整体，并无法从部分观察中识别完整的物体。 在本文中，我们提出了InstaScene，一种新的整体3D感知范式，主要目标是分解任意实例的同时确保完整重建。 为了实现精确分解，我们开发了一种新颖的空间对比学习方法，通过跨视角追踪每个实例的光栅化过程，显著增强了杂乱场景中的语义监督。 为了克服有限观察带来的不完整性，我们引入了原位生成，利用有价值的观察和几何线索，有效地引导3D生成模型重建与现实世界无缝对齐的完整实例。 在复杂的真实世界和合成场景中的场景分解和物体补全实验表明，我们的方法在实现优越分解精度的同时，生成了几何上真实且视觉完整的物体。 显示英文摘要

摘要： Humans can naturally identify and mentally complete occluded objects in cluttered environments. However, imparting similar cognitive ability to robotics remains challenging even with advanced reconstruction techniques, which models scenes as undifferentiated wholes and fails to recognize complete object from partial observations. In this paper, we propose InstaScene, a new paradigm towards holistic 3D perception of complex scenes with a primary goal: decomposing arbitrary instances while ensuring complete reconstruction. To achieve precise decomposition, we develop a novel spatial contrastive learning by tracing rasterization of each instance across views, significantly enhancing semantic supervision in cluttered scenes. To overcome incompleteness from limited observations, we introduce in-situ generation that harnesses valuable observations and geometric cues, effectively guiding 3D generative models to reconstruct complete instances that seamlessly align with the real world. Experiments on scene decomposition and object completion across complex real-world and synthetic scenes demonstrate that our method achieves superior decomposition accuracy while producing geometrically faithful and visually intact objects.