DKPMV: Dense Keypoints Fusion from Multi-View RGB Frames for 6D Pose Estimation of Textureless Objects

• URL: http://arxiv.org/abs/2510.10933v1
• Date: Mon, 13 Oct 2025 02:45:55 GMT
• Title: DKPMV: Dense Keypoints Fusion from Multi-View RGB Frames for 6D Pose Estimation of Textureless Objects
• Authors: Jiahong Chen, Jinghao Wang, Zi Wang, Ziwen Wang, Banglei Guan, Qifeng Yu,
• Abstract summary: We propose DKPMV, a pipeline that achieves dense keypoint-level fusion.<n>We enhance the keypoint network with attentional aggregation and symmetry-aware training.<n>Experiments on the ROBI dataset demonstrate that DKPMV outperforms state-of-the-art multi-view RGB approaches.
• Score: 18.011730388391232
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 6D pose estimation of textureless objects is valuable for industrial robotic applications, yet remains challenging due to the frequent loss of depth information. Current multi-view methods either rely on depth data or insufficiently exploit multi-view geometric cues, limiting their performance. In this paper, we propose DKPMV, a pipeline that achieves dense keypoint-level fusion using only multi-view RGB images as input. We design a three-stage progressive pose optimization strategy that leverages dense multi-view keypoint geometry information. To enable effective dense keypoint fusion, we enhance the keypoint network with attentional aggregation and symmetry-aware training, improving prediction accuracy and resolving ambiguities on symmetric objects. Extensive experiments on the ROBI dataset demonstrate that DKPMV outperforms state-of-the-art multi-view RGB approaches and even surpasses the RGB-D methods in the majority of cases. The code will be available soon.

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