碰撞感知与观测对齐的面向对象的点云场景重建

中文摘要 AI 辅助

面向对象的场景重建需要在完成部分对象观测的同时保持度量对齐并避免与周围环境发生碰撞。现有的基于生成的方法通常以图像为条件，存在尺度模糊和几何约束不足的问题。我们提出了COOL，一个用于碰撞感知与观测对齐重建的框架。基于对象生成模型，COOL以实例和背景点云为条件进行生成。实例几何将生成锚定在场景坐标中，而背景几何为场景一致的完成提供局部上下文。我们进一步引入了显式的碰撞损失，并使用联合优化和重采样来减少推理过程中的碰撞。在3D-Front和Scan2CAD上的实验展示了强大的场景级保真度、观测对齐和碰撞减少。此外，额外的研究验证了其对掩码错误的鲁棒性及其在真实世界场景复制中的适用性。

英文摘要

Object-centric scene reconstruction requires completing partial object observations while preserving metric alignment and avoiding collisions with the surrounding. Existing generation-based methods are often image-conditioned and suffer from scale ambiguity and insufficient geometric constraints. We propose COOL, a framework for COllision-aware and Observation-aLigned reconstruction. Based on an object generation model, COOL conditions the generation on instance and background point clouds. Instance geometry anchors generation in scene coordinates, while background geometry provides local context for scene-consistent completion. We further introduce an explicit collision loss and use joint optimization and resampling to reduce collisions during inference. Experiments on 3D-Front and Scan2CAD demonstrate strong scene-level fidelity, observation alignment, and collision reduction. Moreover, additional studies validate its robustness to mask errors and its applicability to real-world scene replicas.

发表机构

• Zhejiang University（浙江大学）

机构由 AI 辅助整理，请以论文原文为准。