核心概念
The proposed AG-Pose method can adaptively detect a set of sparse keypoints to represent the geometric structures of different object instances, and efficiently integrate local and global geometric information into keypoint features to establish robust keypoint-level correspondences for accurate 6D pose estimation of unseen instances.
摘要
The content discusses a novel Instance-Adaptive and Geometric-Aware Keypoint Learning method (AG-Pose) for category-level 6D object pose estimation. The key highlights are:
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Existing dense correspondence-based methods do not explicitly consider the local and global geometric information of different instances, resulting in poor generalization ability to unseen instances with significant shape variations.
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The proposed AG-Pose method has two key designs:
- Instance-Adaptive Keypoint Detection (IAKD) module: Adaptively detects a set of sparse keypoints to represent the geometric structures of different instances.
- Geometric-Aware Feature Aggregation (GAFA) module: Efficiently integrates local and global geometric information into keypoint features to establish robust keypoint-level correspondences.
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The IAKD module converts category-shared learnable queries into instance-adaptive detectors by aggregating object features. It also employs a diversity loss and an object-aware chamfer distance loss to encourage the keypoints to be well distributed on the object surface.
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The GAFA module incorporates local geometric information by aggregating features from the nearest neighbors of each keypoint, and global geometric information by integrating relative positional embeddings and global features.
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Experiments on the CAMERA25 and REAL275 datasets show that the proposed AG-Pose outperforms state-of-the-art methods by a large margin without using category-specific shape priors.
統計資料
The shapes of different instances vary significantly, making it challenging to generalize to unseen instances.
Existing dense correspondence-based methods tend to generate numerous incorrect correspondences for instances with large shape variations.
引述
"To deal with this problem, we propose a novel Instance-Adaptive and Geometric-Aware Keypoint Learning method for category-level 6D object pose estimation (AG-Pose), which includes two key designs: (1) The first design is an Instance-Adaptive Keypoint Detection module, which can adaptively detect a set of sparse keypoints for various instances to represent their geometric structures. (2) The second design is a Geometric-Aware Feature Aggregation module, which can efficiently integrate the local and global geometric information into keypoint features."