Haikuan Ning

Xudong Zhang, Shuang Gao, Xiaohu Nan et al.

Camera localization is a classical computer vision task that serves various Artificial Intelligence and Robotics applications. With the rapid developments of Deep Neural Networks (DNNs), end-to-end visual localization methods are prosperous in recent years. In this work, we focus on the scene coordinate prediction ones and propose a network architecture named as Structure Guidance Learning (SGL) which utilizes the receptive branch and the structure branch to extract both high-level and low-level features to estimate the 3D coordinates. We design a confidence strategy to refine and filter the predicted 3D observations, which enables us to estimate the camera poses by employing the Perspective-n-Point (PnP) with RANSAC. In the training part, we design the Bundle Adjustment trainer to help the network fit the scenes better. Comparisons with some state-of-the-art (SOTA) methods and sufficient ablation experiments confirm the validity of our proposed architecture.

Shuang Gao, Jixiang Wan, Yishan Ping et al.

High-precision camera re-localization technology in a pre-established 3D environment map is the basis for many tasks, such as Augmented Reality, Robotics and Autonomous Driving. The point-based visual re-localization approaches are well-developed in recent decades, but are insufficient in some feature-less cases. In this paper, we design a complete pipeline for camera pose refinement with points and lines, which contains the innovatively designed line extracting CNN named VLSE, the line matching and the pose optimization approaches. We adopt a novel line representation and customize a hybrid convolution block based on the Stacked Hourglass network, to detect accurate and stable line features on images. Then we apply a geometric-based strategy to obtain precise 2D-3D line correspondences using epipolar constraint and reprojection filtering. A following point-line joint cost function is constructed to optimize the camera pose with the initial coarse pose from the pure point-based localization. Sufficient experiments are conducted on open datasets, i.e, line extractor on Wireframe and YorkUrban, localization performance on InLoc duc1 and duc2, to confirm the effectiveness of our point-line joint pose optimization method.