H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields
<|reference_start|>H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields: 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.<|reference_end|>
@article{park2024h2o-sdf:,
    title={H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object
  Surface Fields},
    author={Minyoung Park, Mirae Do, YeonJae Shin, Jaeseok Yoo, Jongkwang Hong,
  Joongrock Kim, Chul Lee},
    journal={arXiv preprint arXiv:2402.08138},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08138},
    primaryClass={cs.CV}
}