Calibnet
A self-supervised deep learning model for extrinsic calibration between LiDAR and camera sensors using 3D spatial transformer networks.
What is Calibnet?
CalibNet is a self-supervised deep learning model for extrinsic calibration between LiDAR and camera sensors in autonomous systems. It estimates the 6-degree-of-freedom rigid transformation that aligns LiDAR point clouds with camera images without requiring manually labeled calibration data. The model uses 3D spatial transformer networks to learn this alignment through a point cloud distance loss function.
Researchers and engineers working on autonomous vehicles, robotics, or multi-sensor perception systems who need accurate and automated calibration between LiDAR and cameras.
It eliminates the need for tedious manual calibration or labeled datasets by using a self-supervised approach, provides an end-to-end trainable deep learning solution, and integrates with popular frameworks like TensorFlow and ResNet for ease of implementation.
Overview
[DEPRECATED] Self-Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
Use Cases
Best For
- Automating LiDAR-camera calibration for autonomous vehicle perception stacks
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