Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images
| Autor: | Abdeslam Boularias, Jean-Philippe Mercier, Chaitanya Mitash, Philippe Giguère |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Deep learning Feature extraction Learning object Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Object (computer science) Real image 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Segmentation Computer vision Artificial intelligence business Pose |
| Zdroj: | ICRA |
| Popis: | Accurate pose estimation is often a requirement for robust robotic grasping and manipulation of objects placed in cluttered, tight environments, such as a shelf with multiple objects. When deep learning approaches are employed to perform this task, they typically require a large amount of training data. However, obtaining precise 6 degrees of freedom for ground-truth can be prohibitively expensive. This work therefore proposes an architecture and a training process to solve this issue. More precisely, we present a weak object detector that enables localizing objects and estimating their 6D poses in cluttered and occluded scenes. To minimize the human labor required for annotations, the proposed detector is trained with a combination of synthetic and a few weakly annotated real images (as little as 10 images per object), for which a human provides only a list of objects present in each image (no time-consuming annotations, such as bounding boxes, segmentation masks and object poses). To close the gap between real and synthetic images, we use multiple domain classifiers trained adversarially. During the inference phase, the resulting class-specific heatmaps of the weak detector are used to guide the search of 6D poses of objects. Our proposed approach is evaluated on several publicly available datasets for pose estimation. We also evaluated our model on classification and localization in unsupervised and semi-supervised settings. The results clearly indicate that this approach could provide an efficient way toward fully automating the training process of computer vision models used in robotics. |
| Databáze: | OpenAIRE |
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