VD-NeRF:可见度意识脱的神经辐射场用于视图一致的编辑和高频再照明
概括
本研究介绍VD-NeRF,这是一种用于现实的3D场景编辑和重新照明的新方法. 它有效地将外观与几何脱,使得一致的编辑和高频重新照明能够提高可见度意识.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 人工智能的人工智能
背景情况:
- 神经辐射场 (NeRFs) 在新的视图合成方面表现出色,但由于集成的几何和外观编码,在场景操纵方面遇到了困难.
- 现有的NeRF编辑方法经常产生视图不一致的结果,并且在建模高频照明效果方面存在局限性.
- 在NeRF中的当前照明表示,例如球体波或低分辨率地图,对于复杂的重新照明场景是不够的.
研究的目的:
- 开发一种视觉意识的方法 (VD-NeRF) 来解脱3D场景中的视觉独立和视觉依赖的外观.
- 为了在NeRF中实现一致的外观编辑和高频环境重新照明.
- 通过整合可见性信息来提高场景分解和重新照明的质量.
主要方法:
- 从输入场景重建一个明确的网格,使用签名距离函数.
- 使用脱的NeRF,使用脱的特征将视图独立的外观附加到网格中.
- 使用混合照明表示,具有明确的可学习环境地图和隐性照明网络,进行全面的重新照明.
主要成果:
- 在编辑场景外观后获得了视图一致的结果.
- 通过支持新型环境地图和隐性照明网络,实现了高频环境重新照明.
- 通过可见度意识,证明了更好的分解和重新照明质量.
- 在编辑和重新照明任务方面,在数量和质量上优于以前的方法.
结论:
- VD-NeRF成功地将场景属性脱,以提高可编辑性和重新点亮性.
- 混合照明表示和可见度意识显著改善染和编辑结果.
- 拟议的方法为复杂的3D场景操纵和现实的重新照明应用提供了强大的解决方案.
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