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OccScene:基于语义占用的跨任务相互学习以生成3D场景
IEEE transactions on pattern analysis and machine intelligence
|August 25, 2025
概括
使用新的相互学习框架统一3D场景生成和感知. 这种方法通过整合语义占用和文本提示来增强现实的场景创建和改进3D感知.
科学领域:
- 计算机视觉
- 人工智能
- 三维图形
背景情况:
- 扩散模型在3D场景生成和感知方面表现出色,但通常是分开的.
- 现有的方法通常使用合成数据增强来完成感知任务,从而限制了整合.
研究的目的:
- 提出一个统一的3D感知和生成框架OccScene.
- 通过相互学习实现生成质量和感知精度的协同改进.
主要方法:
- 开发了OccScene,一个以语义占用和文本提示为指导的联合培训传播框架.
- 引入基于Mamba的双对齐模块,以整合细粒度语义和几何作为感知先验.
- 能够实现相互学习,从而改善感知,反之亦然.
主要成果:
- 从文本提示生成现实和一致的3D场景.
- 在语义占用预测方面表现出显著的性能改善.
- 在各种室内和室外场景中验证有效性.
结论:
- 在一个单一的框架中成功整合了3D场景生成和感知.
- 相互学习模式对这两个任务都有显著的好处.
- 为开发先进的3D理解和创建系统提供了新的方向.
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