Self-training Room Layout Estimation via Geometry-aware Ray-casting
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913439399542784 |
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| author | Solarte, Bolivar Wu, Chin-Hsuan Jhang, Jin-Cheng Lee, Jonathan Tsai, Yi-Hsuan Sun, Min |
| author_facet | Solarte, Bolivar Wu, Chin-Hsuan Jhang, Jin-Cheng Lee, Jonathan Tsai, Yi-Hsuan Sun, Min |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15041 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-training Room Layout Estimation via Geometry-aware Ray-casting Solarte, Bolivar Wu, Chin-Hsuan Jhang, Jin-Cheng Lee, Jonathan Tsai, Yi-Hsuan Sun, Min Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Self-training Room Layout Estimation via Geometry-aware Ray-casting |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2407.15041 |