Summary of Self-training Room Layout Estimation Via Geometry-aware Ray-casting, by Bolivar Solarte et al.
Self-training Room Layout Estimation via Geometry-aware Ray-casting
by Bolivar Solarte, Chin-Hsuan Wu, Jin-Cheng Jhang, Jonathan Lee, Yi-Hsuan Tsai, Min Sun
First submitted to arxiv on: 21 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel self-training framework for room layout estimation models that doesn’t require labeled data. The approach uses a ray-casting method to combine estimates from different viewing positions, generating reliable pseudo-labels for self-training. This geometry-aware framework prioritizes spatial proximity to the camera view and enforces multi-view consistency along all ray directions, making it effective in handling complex room geometries and occluded walls. By leveraging this framework, state-of-the-art layout models can be improved without relying on human annotation, as demonstrated by evaluation on publicly available datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train computer models that can estimate the layout of a room. The model uses information from different angles to get a better understanding of the space, even when there are obstacles or complex shapes. This approach allows for more accurate predictions without needing human help, which could be useful in many areas such as robotics or virtual reality. |
Keywords
» Artificial intelligence » Self training
