Self-training Room Layout Estimation via Geometry-aware Ray-casting

• URL: http://arxiv.org/abs/2407.15041v1
• Date: Sun, 21 Jul 2024 03:25:55 GMT
• Title: Self-training Room Layout Estimation via Geometry-aware Ray-casting
• Authors: Bolivar Solarte, Chin-Hsuan Wu, Jin-Cheng Jhang, Jonathan Lee, Yi-Hsuan Tsai, Min Sun,
• Abstract summary: We introduce a 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.
• Score: 27.906107629563852
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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