In this work, we first define three desirable properties of a weakly supervised method: local consistency, semantic fidelity, and completeness. This is in contrast to earlier work that used only a single stage − training one segmentation network on image labels − which was abandoned due to inferior segmentation accuracy. However, this has come at the cost of increased model complexity and sophisticated multi-stage training procedures. with only image-level labels available for training. Recent years have seen a rapid growth in new approaches improving the accuracy of semantic segmentation in a weakly supervised setting, i.e. Single-Stage Semantic Segmentation from Image Labels (CVPR 2020) As an application we use this for robotic bin picking of transparent objects. Our method achieves instance segmentation on cluttered, transparent objects in various scene and background conditions, demonstrating an improvement over traditional image-based approaches. We use a polarization camera to capture multi-modal imagery and couple this with a unique deep learning backbone for processing polarization input data. This paper reframes the problem of transparent object segmentation into the realm of light polarization, i.e., the rotation of light waves. Transparent objects lack texture of their own, adopting instead the texture of scene background. Segmentation of transparent objects is a hard, open problem in computer vision. We believe that both Trans10K and TransLab have important contributions to both the academia and industry, facilitating future researches and applications.ĭeep Polarization Cues for Transparent Object Segmentation (CVPR 2020)Īgastya Kalra, Vage Taamazyan, Supreeth Krishna Rao, Kartik Venkataraman, Ramesh Raskar, Achuta Kadambi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. For example, TransLab significantly outperforms 20 recent object segmentation methods based on deep learning, showing that this task is largely unsolved. Extensive experiments and ablation studies demonstrate the effectiveness of Trans10K and validate the practicality of learning object boundary in TransLab. To evaluate the effectiveness of Trans10K, we propose a novel boundary-aware segmentation method, termed TransLab, which exploits boundary as the clue to improve segmentation of transparent objects. The transparent objects in Trans10K are extremely challenging due to high diversity in scale, viewpoint and occlusion as shown in Fig. To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10,428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets. They either possess limited sample size such as merely a thousand of images without manual annotations, or they generate all images by using computer graphics method (i.e. Besides the technical difficulty of this task, only a few previous datasets were specially designed and collected to explore this task and most of the existing datasets have major drawbacks. Segmenting transparent objects is challenging because these objects have diverse appearance inherited from the image background, making them had similar appearance with their surroundings. Transparent objects such as windows and bottles made by glass widely exist in the real world. Segmenting Transparent Objects in the Wild (ECCV 2020)Įnze Xie, Wenjia Wang, Wenhai Wang, Mingyu Ding, Chunhua Shen, Ping Luo The following selected data were taken from the financial statements of Robinson Inc.
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