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.
@article{arxiv.2402.08138,
title = {H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields},
author = {Minyoung Park and Mirae Do and YeonJae Shin and Jaeseok Yoo and Jongkwang Hong and Joongrock Kim and Chul Lee},
journal= {arXiv preprint arXiv:2402.08138},
year = {2024}
}