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CDNet: Cross-Domain Description and Detection for 2D - 3D Learning Local Features
  • Zhenqiang Li
Zhenqiang Li
Qufu Normal University - Rizhao Campus
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By integrating descriptors with keypoint detectors, we can unlock the possibility of achieving comprehensive image matching and retrieval, which presents significant implications for various modern applications in computer vision and image processing. Despite the numerous proposals for learning-based feature detection and keypoint description in 2D or 3D, the matching between 2D images and 3D point clouds has not received thorough investigation. In this work, we propose a two-branch fully convolutional network framework that maps 2D images and 3D point clouds into a latent space for feature description and feature point detection. Our model leverages two parallel branches, one for extracting features from 2D images and the other from 3D point clouds, while facilitating information exchange through weight sharing. This approach enables us to fully exploit the correlations between 2D images and 3D point clouds, enhancing the expressive power of features and achieving more accurate and robust performance in image matching and retrieval tasks. Additionally, we have designed a novel loss function to enhance descriptor performance and enable more accurate keypoint detection. Finally, we extensively evaluate our model on the SceneNN and 3DMatch datasets, demonstrating its strong performance in accurate and efficient 2D-3D image matching. Our findings have significant implications for various applications, including augmented reality, autonomous navigation, and 3D reconstruction.