Object-Pose Estimation With Neural Population Codes

2025-02-19

AI Summary

In robotic assembly, estimating the 6D pose of symmetric objects suffers from rotational ambiguity; existing approaches rely on multi-hypothesis evaluation or probabilistic modeling, incurring high computational cost and hindering end-to-end optimization. Method: This paper introduces neural population coding—the first such application to 6D pose estimation—to explicitly represent rotation in a continuous, symmetry-robust manner, enabling a direct, differentiable, end-to-end mapping from sensory input (single-frame grayscale images) to 6D pose without explicit symmetry handling or post-processing. Contribution/Results: The method achieves state-of-the-art symmetry-aware surface distance (SADD) accuracy of 84.7% on the T-LESS dataset, with inference time of only 3.2 ms on an Apple M1 CPU—outperforming direct regression by 15 percentage points—demonstrating superior accuracy, real-time capability, and trainability.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Visual Reasoning & Symbolic Representations
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Object-pose estimation in robotics
• Ambiguity from object symmetry
• Neural population codes for rotation

Innovation

• Neural population codes for rotation
• End-to-end learning enabled
• Fast, accurate pose estimation