Object Pose Estimation Based on Multi-precision Vectors and Seg-Driven PnP

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yulin Wang, Hongli Li, Chen Luo
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引用次数: 0

Abstract

Object pose estimation based on a single RGB image has wide application potential but is difficult to achieve. Existing pose estimation involves various inference pipelines. One popular pipeline is to first use Convolutional Neural Networks (CNN) to predict 2D projections of 3D keypoints in a single RGB image and then calculate the 6D pose via a Perspective-n-Point (PnP) solver. Due to the gap between synthetic data and real data, the model trained on synthetic data has difficulty predicting the 6D pose accurately when applied to real data. To address the acute problem, we propose a two-stage pipeline of object pose estimation based upon multi-precision vectors and segmentation-driven (Seg-Driven) PnP. In keypoint localization stage, we first develop a CNN-based three-branch network to predict multi-precision 2D vectors pointing to 2D keypoints. Then we introduce an accurate and fast Keypoint Voting scheme of Multi-precision vectors (KVM), which computes low-precision 2D keypoints using low-precision vectors and refines 2D keypoints on mid- and high-precision vectors. In the pose calculation stage, we propose Seg-Driven PnP to refine the 3D Translation of poses and get the optimal pose by minimizing the non-overlapping area between segmented and rendered masks. The Seg-Driven PnP leverages 2D segmentation trained on real images to improve the accuracy of pose estimation trained on synthetic data, thereby reducing the synthetic-to-real gap. Extensive experiments show our approach materially outperforms state-of-the-art methods on LM and HB datasets. Importantly, our proposed method works reasonably well for weakly textured and occluded objects in diverse scenes.

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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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