In this paper  we investigate the problems that Convolutional Neural Networks (CNN) based pose estimators have with symmetric objects. We find that the CNN's output representation  has to form a closed loop when continuously rotating by one step of symmetry. Otherwise the CNN has to learn an uncontinuous function. On a 1-DOF toy example we show that commonly used representations do not fulfill this demand and analyze the problems caused thereby. In particular we find, that the popular min-over-symmetries approach for creating a symmetry aware loss tends not to work well with gradient based optimization, i.e. deep learning. We propose a representation called "closed symmetry loop" ' (csl) from these insights, where the angle of relevant vectors is multiplied by the symmetry order and then generalize it to 6-DOF. The representation extends our previous algorithm  including a method to disambiguate symmetric equivalents during the final pose estimation. The algorithm handles continuous rotational symmetry ( i.e. a bottle) and discrete rotational symmetry ( general boxes, boxes with a square face, uniform prims, but no cubes ). It is evaluated on the T-LESS dataset  .
In this paper , we investigate the problems that Convolutional Neural Networks (CNN) -based pose estimators have with symmetric objects. We considered the value of the CNN's output representation when continuously rotating the object and found that it has to form a closed loop after each step of symmetry. Otherwise , the CNN (which is itself a continuous function) has to replicate an uncontinuous function. On a 1-DOF toy example we show that commonly used representations do not fulfill this demand and analyze the problems caused thereby. In particular , we find that the popular min-over-symmetries approach for creating a symmetry-aware loss tends not to work well with gradient-based optimization, i.e. deep learning. We propose a representation called "closed symmetry loop" (csl) from these insights, where the angle of relevant vectors is multiplied by the symmetry order and then generalize it to 6-DOF. The representation extends our algorithm from Richter-Klug, ICVS, 2019 including a method to disambiguate symmetric equivalents during the final pose estimation. The algorithm handles continuous rotational symmetry ( e.g. a bottle) and discrete rotational symmetry ( e.g. a 4-fold symmetric box ). It is evaluated on the T-LESS dataset , where it reaches state-of-the-art for unrefining RGB-based methods .
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when continuously rotating the object and found that it
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when continuously rotating by one
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the CNN has to learn
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previous algorithm
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Richter-Klug, ICVS, 2019
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i.e.
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general boxes, boxes with a square face, uniform prims, but no cubes
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, where it reaches state-of-the-art for unrefining RGB-based methods