Implement and optimize deep learning models specifically designed for LiDAR point clouds, including PointNet, PointNet++, and other 3D neural network architectures. Proficiency in deep learning frameworks (PyTorch and TensorFlow) with hands-on experience implementing detection models (YOLO, Faster R-CNN, SSD, RetinaNet, Detectron, etc.).
Design and implement computer vision algorithms for object detection and segmentation using camera and LiDAR data fusion.
Develop deep learning models for 2D and 3D object detection, including implementation and optimization of YOLO, Faster R-CNN, SSD, and transformer-based architectures.
Create and optimize LiDAR point cloud processing pipelines using PCL and Open3D for 3D object detection and segmentation.
Implement sensor fusion techniques to combine camera and LiDAR data for enhanced object detection accuracy.
Develop instance and semantic segmentation algorithms using state-of-the-art models like Mask R-CNN, U-Net, and DeepLab.
Implement and optimize deep learning models specifically designed for LiDAR point clouds, including PointNet, PointNet++, and other 3D neural network architectures.
Develop robust perception algorithms that maintain performance in adverse weather conditions such as rain, snow, fog, and low-light scenarios.
Build and maintain computer vision pipelines using OpenCV for image preprocessing, feature extraction, and geometric transformations.
Design and implement multi-object tracking systems using Kalman filtering, SORT, and DeepSORT algorithms.
Work with ROS2 for integration and deployment of perception algorithms.
Optimize deep learning models for edge deployment and real-time inference performance.
Develop robust evaluation metrics and testing frameworks for object detection systems.
Collaborate with cross-functional teams to integrate perception algorithms into larger autonomous systems.
Stay up-to-date with industry trends and emerging technologies to innovate and improve perception systems.
What You Will Bring:
Strong expertise in computer vision and deep learning for object detection and segmentation tasks.
Proficiency in deep learning frameworks (PyTorch and TensorFlow) with hands-on experience implementing detection models (YOLO, Faster R-CNN, SSD, RetinaNet, Detectron, etc.).
Extensive experience with OpenCV for image processing and computer vision applications.
Solid background in 3D perception using LiDAR point clouds; proficiency with PCL and Open3D libraries.
Familiarity with LiDAR-specific deep learning models such as PointNet, PointNet++, VoxelNet, and other point cloud neural network architectures.
Experience in developing and improving perception models for adverse weather conditions (rain, snow, fog) including domain adaptation and robust feature extraction techniques.
Experience with sensor fusion techniques for combining camera and LiDAR data streams.
Strong programming skills in Python and C++ for algorithm development and optimization.
Experience with model optimization techniques for real-time inference.
Familiarity with 3D geometry, coordinate transformations, and spatial data processing.
Knowledge of evaluation metrics for object detection and tracking systems (mAP, IoU, custom metrics, etc.).