Advanced techniques using Neural Radiance Fields (NeRF), Signed Distance Fields (SDF), and Occupancy Fields have recently emerged as solutions for 3D indoor scene reconstruction. We introduce a novel two-phase learning approach, H2O-SDF, that discriminates between object and non-object regions within indoor environments. This method achieves a nuanced balance, carefully preserving the geometric integrity of room layouts while also capturing intricate surface details of specific objects. A cornerstone of our two-phase learning framework is the introduction of the Object Surface Field (OSF), a novel concept designed to mitigate the persistent vanishing gradient problem that has previously hindered the capture of high-frequency details in other methods. Our proposed approach is validated through several experiments that include ablation studies.

翻译：利用神经辐射场（NeRF）、符号距离场（SDF）和占据场等先进技术已成为三维室内场景重建的解决方案。我们提出了一种新颖的两阶段学习方法H2O-SDF，该方法能够区分室内环境中的物体区域与非物体区域。该方法实现了精细的平衡，在保持房间布局几何完整性的同时，还能捕捉特定物体的复杂表面细节。我们两阶段学习框架的核心是引入物体表面场（OSF），这是一种新颖的概念，旨在缓解持续存在的梯度消失问题——该问题此前阻碍了其他方法对高频细节的捕捉。通过包括消融研究在内的多项实验，我们验证了所提出方法的有效性。