Object-Pose Estimation With Neural Population Codes

• URL: http://arxiv.org/abs/2502.13403v1
• Date: Wed, 19 Feb 2025 03:23:43 GMT
• Title: Object-Pose Estimation With Neural Population Codes
• Authors: Heiko Hoffmann, Richard Hoffmann,
• Abstract summary: Object symmetry complicates the direct mapping of sensory input to object rotation. We show that representing object rotation with a neural population code overcomes these limitations. We achieve inference in 3.2 milliseconds on an Apple M1 CPU.
• Score: 4.557963624437784
• License:
• Abstract: Robotic assembly tasks require object-pose estimation, particularly for tasks that avoid costly mechanical constraints. Object symmetry complicates the direct mapping of sensory input to object rotation, as the rotation becomes ambiguous and lacks a unique training target. Some proposed solutions involve evaluating multiple pose hypotheses against the input or predicting a probability distribution, but these approaches suffer from significant computational overhead. Here, we show that representing object rotation with a neural population code overcomes these limitations, enabling a direct mapping to rotation and end-to-end learning. As a result, population codes facilitate fast and accurate pose estimation. On the T-LESS dataset, we achieve inference in 3.2 milliseconds on an Apple M1 CPU and a Maximum Symmetry-Aware Surface Distance accuracy of 84.7% using only gray-scale image input, compared to 69.7% accuracy when directly mapping to pose.

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