Self-training Room Layout Estimation via Geometry-aware Ray-casting
<|reference_start|>Self-training Room Layout Estimation via Geometry-aware Ray-casting: In this paper, we introduce a novel geometry-aware self-training framework for room layout estimation models on unseen scenes with unlabeled data. Our approach utilizes a ray-casting formulation to aggregate multiple estimates from different viewing positions, enabling the computation of reliable pseudo-labels for self-training. In particular, our ray-casting approach enforces multi-view consistency along all ray directions and prioritizes spatial proximity to the camera view for geometry reasoning. As a result, our geometry-aware pseudo-labels effectively handle complex room geometries and occluded walls without relying on assumptions such as Manhattan World or planar room walls. Evaluation on publicly available datasets, including synthetic and real-world scenarios, demonstrates significant improvements in current state-of-the-art layout models without using any human annotation.<|reference_end|>
@article{solarte2024self-training,
    title={Self-training Room Layout Estimation via Geometry-aware Ray-casting},
    author={Bolivar Solarte, Chin-Hsuan Wu, Jin-Cheng Jhang, Jonathan Lee,
  Yi-Hsuan Tsai, Min Sun},
    journal={arXiv preprint arXiv:2407.15041},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15041},
    primaryClass={cs.CV cs.AI}
}