Awesome-Deep-Camera-Calibration

KangLiao929/Awesome-Deep-Camera-Calibration is ranked 14,129 among these 8 peers; WASasquatch/comfyui-plugins is ranked 14,117.

Awesome-Deep-Camera-Calibration ** Popular calibration objectives, models, and extended applications in camera calibration ** More content and details can be found in our Survey Paper: Deep Learning for Camera Calibration and Beyond: A Survey. Project and Course using Our Survey 📢 News Our recent work **Puffin** can unify the camera-centric understanding (camera calibration, pose estimation) and generation (camera-controllable T2I and I2I generation) within a cohesive multimodal framework. It enables more precise understanding and generation performance by our proposed *thinking

with camera*, and provides insights on the meaningful mutual effect among multimodal tasks. If you are interested in the camera-related 3D vision, photography, embodied AI, and spatial intelligence, please check out more details here. 24: Update the Novel Calibration Representations in the learning-based camera calibration, which show the potential to replace the traditional calibration objectives for neural networks. 24: Update the literature reviews for 2023 and 2024 (more than 100 new papers! Please refer more details to our

arXiv-v3 version. 05: Update the survey paper (supplementary material) with the evaluation on the constructed benchmark. 3-Calibration with Reconstruction) with more technical discussions with NeRF, especially in the camera parameter initialization. 04: More details about the calibrated camera parameters in our Benchmark are updated. Please refer to the dataset link and more details in Benchmark. 19: The survey of the arXiv version is online. 📖Basics Multiple View Geometry in Computer Vision - Hartley, R. (2004) A Flexible New Technique

for Camera Calibration - Zhengyou Zhang. (2000) 📊Taxonomy and Statistics ** The structural and hierarchical taxonomy of camera calibration with deep learning. Some classical methods are listed under each category. ** ** A concise milestone of deep learning-based camera calibration methods. ** We classify all methods based on the uncalibrated camera model and its extended applications: standard model, distortion model, cross-view model, and cross-sensor model. ** A statistic analysis of deep learning-based camera calibration methods. ** We summarize all literature based on the

number of publications per year, calibration objectives, simulation of the dataset, and learning strategy. 📁Benchmark ** Overview of our collected benchmark, which covers all models reviewed in this survey. ** In this dataset, the image and video were derived from diverse cameras under different environments. The accurate ground truth and label are provided for each data. Please refer to the dataset link and more details in Benchmark. 🏁Novel Calibration Representations ** Novel calibration representations are designed for replacing the traditional calibration objectives. ** Recent

learning-based camera calibration works tend to design a novel geometry field to replace the traditional camera parameters (*i. *, intrinsics and extrinsics) as the new learning target, which is inspired by the prior of camera models or the perspective properties of captured images, such as the distortion distribution map, perspective field, incidence field, camera rays, and camera image, etc. These fields represent a pixel-wise or patch-wise parametrization of the intrinsic and/or extrinsic invariants. They show an explicit

relationship to the image details and are learning-friendly for neural networks. |Distortion coefficients|Caffe|AlexNet| |2016|RSSW|Deep image