Abstract:
Due to constraints from imaging devices, the most effective method for estimating the pose of space target RGB images is to establish a 2-D–3-D correspondence and then us...Show MoreMetadata
Abstract:
Due to constraints from imaging devices, the most effective method for estimating the pose of space target RGB images is to establish a 2-D–3-D correspondence and then use the perspective-n-point (PnP) algorithm for pose recovery. However, traditional PnP algorithms are not differentiable, which hinders their integration with neural network training. Although recent work attempts to make PnP partially differentiable during the 2-D–3-D matching stage by mathematical methods, this leads to increased inevitably computational costs. To this end, we propose a scale-consistent learnable PnP (SCLP) network that facilitates end-to-end pose estimation for space targets. Our method incorporates a sparse keypoint learnable PnP (SKL-PnP) layer within a multiscale network, enabling PnP to function as a differentiable layer that integrates seamlessly with preceding neural components. Additionally, we also sample the 2-D–3-D correspondences to obtain sparse keypoint pairs, achieving a lightweight single-stage 6-D pose estimation algorithm. To manage the significant scale variations in space target images, we introduce Gaussian perception sampling (GPS) by assigning instances to different pyramid levels based on size. Furthermore, we propose a scale consistency regularization (SCR) module that aligns downsized feature maps with original ones to better address scale differences. Experimental results demonstrate that our approach achieves superior accuracy and efficiency on the SPEED and SwissCube datasets, showing significant improvements over state-of-the-art methods.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 63)
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- IEEE Keywords
- Index Terms
- Pose Estimation ,
- Target Space ,
- Pose Estimation Network ,
- Neural Network ,
- Feature Maps ,
- Imaging Variables ,
- Pyramid Level ,
- Convolutional Neural Network ,
- Long Short-term Memory ,
- Point Cloud ,
- Training Strategy ,
- Bounding Box ,
- Kullback-Leibler ,
- Large-scale Changes ,
- Two-stage Method ,
- Feature Pyramid ,
- Large-scale Variation ,
- Feature Pyramid Network ,
- Motion Blur ,
- Human Pose Estimation ,
- Target Density ,
- Baseline Network ,
- Image Resampling ,
- Reprojection Error ,
- Ground Truth Pose ,
- Pose Information ,
- Accuracy Of Pose Estimation ,
- Random Sample Consensus ,
- Translation Vector
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Pose Estimation ,
- Target Space ,
- Pose Estimation Network ,
- Neural Network ,
- Feature Maps ,
- Imaging Variables ,
- Pyramid Level ,
- Convolutional Neural Network ,
- Long Short-term Memory ,
- Point Cloud ,
- Training Strategy ,
- Bounding Box ,
- Kullback-Leibler ,
- Large-scale Changes ,
- Two-stage Method ,
- Feature Pyramid ,
- Large-scale Variation ,
- Feature Pyramid Network ,
- Motion Blur ,
- Human Pose Estimation ,
- Target Density ,
- Baseline Network ,
- Image Resampling ,
- Reprojection Error ,
- Ground Truth Pose ,
- Pose Information ,
- Accuracy Of Pose Estimation ,
- Random Sample Consensus ,
- Translation Vector
- Author Keywords