NaviNeRF++:通过无监督解的表示学习实现可解释的3D重建
IEEE transactions on pattern analysis and machine intelligence
|August 28, 2025
概括
NaviNeRF++通过整合多式大型语言模型和神经辐射场来实现可解释的3D重建. 这种框架可以实现无监督,细粒度的3D解和高质量的重建.
科学领域:
- 计算机视觉
- 人工智能
- 机器学习
背景情况:
- 三维重建对于人工智能与现实世界的互动至关重要,但在语义理解和先前依赖方面面临挑战.
- 现有的方法难以解释3D数据的语义,并且需要广泛的先验知识来控制.
研究的目的:
- 提出一个可解释的3D重建框架.
- 在3D重建中解决语义理解和先前依赖的局限性.
- 为了实现精细的3D解和高质量的重建.
主要方法:
- 多式大型语言模型 (MLLM) 和神经辐射场 (NeRF) 的集成.
- 一个轻量级的2D感知模块,用于解的隐藏空间,由预先训练的解表示学习 (DRL) 模型提供信息.
- 一个基于NeRF的3D导航模块,用于语义因素发现和高质量的重建.
- 一个属性识别模块利用MLLM来识别语义因素的文本概念.
主要成果:
- 该框架以无监督的方式实现可解释的3D重建和细粒度的3D解.
- 保持高质量和视图一致的3D重建.
- 与实证评估中的现有解决方案相比,表现优越.
结论:
- NaviNeRF++提供了一种新的无监督,可解释的3D重建方法.
- 整合MLLM和NeRF推动了3D语义和解领域的发展.
- 这一框架为人工智能与3D环境的理解和交互提供了强有力的解决方案.
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