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.
@article{arxiv.2407.15041,
title = {Self-training Room Layout Estimation via Geometry-aware Ray-casting},
author = {Bolivar Solarte and Chin-Hsuan Wu and Jin-Cheng Jhang and Jonathan Lee and Yi-Hsuan Tsai and Min Sun},
journal= {arXiv preprint arXiv:2407.15041},
year = {2024}
}