Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization
Image-based camera relocalization is an important problem in computer vision and robotics. Recent works utilize convolutional neural networks (CNNs) to regress for pixels in a query image their corresponding 3D world coordinates in the scene. The final pose is then solved via a RANSAC-based optimization scheme using the predicted coordinates. Usually, the CNN is trained with ground truth scene coordinates, but it has also been shown that the network can discover 3D scene geometry automatically by minimizing single-view reprojection loss. However, due to the deficiencies of the reprojection loss, the network needs to be carefully initialized. In this paper, we present a new angle-based reprojection loss, which resolves the issues of the original reprojection loss. With this new loss function, the network can be trained without careful initialization, and the system achieves more accurate results. The new loss also enables us to utilize available multi-view constraints, which further improve performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Camera RelocalizationregressionSimilar Papers 제목 키워드 기반
PoI: Pixel of Interest for Novel View Synthesis Assisted Scene Coordinate Regression
The task of estimating camera poses can be enhanced through novel view synthesis techniques such as NeRF and Gaussian Splatting to increase the diversity and extension of training data. However, these techniques often pr…
DiversityNeRFNovel View SynthesisregressionReprojection Errors as Prompts for Efficient Scene Coordinate Regression
Scene coordinate regression (SCR) methods have emerged as a promising area of research due to their potential for accurate visual localization. However, many existing SCR approaches train on samples from all image region…
feature selectionregressionVisual LocalizationR-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization
Learning-based visual localization methods that use scene coordinate regression (SCR) offer the advantage of smaller map sizes. However, on datasets with complex illumination changes or image-level ambiguities, it remain…
Data AugmentationVisual LocalizationAccelerated Coordinate Encoding: Learning to Relocalize in Minutes using RGB and Poses
Learning-based visual relocalizers exhibit leading pose accuracy, but require hours or days of training. Since training needs to happen on each new scene again, long training times make learning-based relocalization impr…
Homography-Based Loss Function for Camera Pose Regression
Some recent visual-based relocalization algorithms rely on deep learning methods to perform camera pose regression from image data. This paper focuses on the loss functions that embed the error between two poses to perfo…
Deep Learningregression