Monocular Depth Estimation and Segmentation for Transparent Object with Iterative Semantic and Geometric Fusion
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866929739533385728 |
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| author | Liu, Jiangyuan Ma, Hongxuan Guo, Yuxin Zhao, Yuhao Zhang, Chi Sui, Wei Zou, Wei |
| author_facet | Liu, Jiangyuan Ma, Hongxuan Guo, Yuxin Zhao, Yuhao Zhang, Chi Sui, Wei Zou, Wei |
| contents | Transparent object perception is indispensable for numerous robotic tasks. However, accurately segmenting and estimating the depth of transparent objects remain challenging due to complex optical properties. Existing methods primarily delve into only one task using extra inputs or specialized sensors, neglecting the valuable interactions among tasks and the subsequent refinement process, leading to suboptimal and blurry predictions. To address these issues, we propose a monocular framework, which is the first to excel in both segmentation and depth estimation of transparent objects, with only a single-image input. Specifically, we devise a novel semantic and geometric fusion module, effectively integrating the multi-scale information between tasks. In addition, drawing inspiration from human perception of objects, we further incorporate an iterative strategy, which progressively refines initial features for clearer results. Experiments on two challenging synthetic and real-world datasets demonstrate that our model surpasses state-of-the-art monocular, stereo, and multi-view methods by a large margin of about 38.8%-46.2% with only a single RGB input. Codes and models are publicly available at https://github.com/L-J-Yuan/MODEST. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14616 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Monocular Depth Estimation and Segmentation for Transparent Object with Iterative Semantic and Geometric Fusion Liu, Jiangyuan Ma, Hongxuan Guo, Yuxin Zhao, Yuhao Zhang, Chi Sui, Wei Zou, Wei Computer Vision and Pattern Recognition Transparent object perception is indispensable for numerous robotic tasks. However, accurately segmenting and estimating the depth of transparent objects remain challenging due to complex optical properties. Existing methods primarily delve into only one task using extra inputs or specialized sensors, neglecting the valuable interactions among tasks and the subsequent refinement process, leading to suboptimal and blurry predictions. To address these issues, we propose a monocular framework, which is the first to excel in both segmentation and depth estimation of transparent objects, with only a single-image input. Specifically, we devise a novel semantic and geometric fusion module, effectively integrating the multi-scale information between tasks. In addition, drawing inspiration from human perception of objects, we further incorporate an iterative strategy, which progressively refines initial features for clearer results. Experiments on two challenging synthetic and real-world datasets demonstrate that our model surpasses state-of-the-art monocular, stereo, and multi-view methods by a large margin of about 38.8%-46.2% with only a single RGB input. Codes and models are publicly available at https://github.com/L-J-Yuan/MODEST. |
| title | Monocular Depth Estimation and Segmentation for Transparent Object with Iterative Semantic and Geometric Fusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.14616 |