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Camera Calibration
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Camera calibration involves estimating camera parameters(including camera intrinsics and extrinsics) to infer geometric features from captured sequences, which is crucial for computer vision and robotics. Driven by different architectures of the neural network, the researchers have developed two main paradigms for learning-based camera calibration and its applications. One is Regression-based Calibration，Reconstruction-based Calibration is another.
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Monocular One-Shot Metric-Depth Alignment for RGB-Based Robot Grasping
ZeroVO: Visual Odometry with Minimal Assumptions
BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations
Multi-Spectral Gaussian Splatting with Neural Color Representation
Bi-Manual Joint Camera Calibration and Scene Representation
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network
VolE: A Point-cloud Framework for Food 3D Reconstruction and Volume Estimation
ClearLines - Camera Calibration from Straight Lines
Zebrafish Counting Using Event Stream Data
Acquisition of high-quality images for camera calibration in robotics applications via speech prompts
TT3D: Table Tennis 3D Reconstruction
SoccerNet-v3D: Leveraging Sports Broadcast Replays for 3D Scene Understanding
Implementation of a Zed 2i Stereo Camera for High-Frequency Shoreline Change and Coastal Elevation Monitoring
DF-Calib: Targetless LiDAR-Camera Calibration via Depth Flow
Robust LiDAR-Camera Calibration with 2D Gaussian Splatting
AlignDiff: Learning Physically-Grounded Camera Alignment via Diffusion
AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibration
Blind Augmentation: Calibration-free Camera Distortion Model Estimation for Real-time Mixed-reality Consistency
MUSt3R: Multi-view Network for Stereo 3D Reconstruction
High-precision visual navigation device calibration method based on collimator
CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement
Spatiotemporal Multi-Camera Calibration using Freely Moving People
CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics for Metric 3D Shape Recovery
PTZ-Calib: Robust Pan-Tilt-Zoom Camera Calibration
What Really Matters for Learning-based LiDAR-Camera Calibration
Automatic Calibration of a Multi-Camera System with Limited Overlapping Fields of View for 3D Surgical Scene Reconstruction
Deep-BrownConrady: Prediction of Camera Calibration and Distortion Parameters Using Deep Learning and Synthetic Data
Simultaneously Recovering Multi-Person Meshes and Multi-View Cameras with Human Semantics
LUCES-MV: A Multi-View Dataset for Near-Field Point Light Source Photometric Stereo
MV-DUSt3R+: Single-Stage Scene Reconstruction from Sparse Views In 2 Seconds
Data Fusion of Semantic and Depth Information in the Context of Object Detection
BroadTrack: Broadcast Camera Tracking for Soccer
MCBLT: Multi-Camera Multi-Object 3D Tracking in Long Videos
RoMo: Robust Motion Segmentation Improves Structure from Motion
Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.