Robust PnP on a Neuromorphic Processor for Object Pose Estimation
Neuromorphic computing is gaining attention in robotic perception due to its higher energy efficiency. While neural network-based methods can more readily exploit the distributed and parallelized structure of neuromorphic computers, crafting neuromorphic solutions for non-learning tasks is less straightforward. This hampers the usage of neuromorphic computing for perception pipelines that depend on both learning and non-learning components, such as object pose estimation (OPE) where state-of-the-art methods use a deep network to predict 2D landmarks and nonlinear optimization to solve perspective-n-point (PnP). In this paper, we propose a novel neuromorphic-deployable formulation for robu...
| Authors | Tam Ngoc-Bang Nguyen, Mohsi Jawaid, Tat-Jun Chin |
|---|---|
| Primary category | cs.CV |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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