Senior ML / Computer Vision Engineer
ML researcher and engineer with 7+ years building deep learning systems for 3D perception, radar–camera fusion, and semantic scene understanding. PhD in Computer Science (ML) at RPTU Kaiserslautern-Landau, expected 2026. Background in electrical engineering, automation, and control systems.
Camera-radar calibration to 3D scene reconstruction
Robust camera-radar extrinsic calibration method using triple constraint optimization, extended to full 3D scene reconstruction from fused sensor data. Best Industrial Paper Award at ICPRAM 2024.
Panoramic image segmentation using SAM
Panoramic image segmentation leveraging SAM feature encoding with dual-view fusion. Handles the distortion challenges of panoramic imagery by combining perspective and equirectangular representations.
Production deployment of interactive video annotation
Extended and deployed an open-source video tracking/annotation framework (Track-Anything) into a production-ready platform with multi-user authentication, server deployment, and client-facing UI using Gradio. Built for internal use at DFKI.
Panoramic time-of-flight RGB-D dataset for 3D reconstruction
Contributed to creating a panoramic ToF RGB-D dataset for single-capture indoor semantic 3D reconstruction. Published at CVPR 2025 Workshops.
M. Chamseddine, D. Stricker, and J. Rambach, “PanoSAMic: Panoramic Image Segmentation from SAM Feature Encoding and Dual View Fusion,” arXiv preprint, 2026.
M. Chamseddine, J. Rambach, and D. Stricker, “CaRaCTO-3D: From Camera-Radar Calibration to Scene Reconstruction,” SN Computer Science, 2025.
H. Kanayama, M. Chamseddine, S. Guttikonda, S. Okumura, S. Yokota, D. Stricker, and J. Rambach, “ToF-360 — A Panoramic Time-of-Flight RGB-D Dataset for Single Capture Indoor Semantic 3D Reconstruction,” CVPR Workshops, 2025.
M. Chamseddine, J. Rambach, and D. Stricker, “CaRaCTO: Robust Camera-Radar Extrinsic Calibration with Triple Constraint Optimization,” ICPRAM, 2024. Best Industrial Paper
F. Kaufmann, M. Chamseddine, S. Guttikonda, C. Glock, D. Stricker, and J. Rambach, “Ontology-Based Semantic Labeling for RGB-D and Point Cloud Datasets,” EC3, 2023.
I. Brishtel, S. Krauss, M. Chamseddine, J. R. Rambach, and D. Stricker, “Driving Activity Recognition Using UWB Radar and Deep Neural Networks,” Sensors, 2023.
M. Chamseddine, J. Rambach, D. Stricker, and O. Wasenmüller, “Ghost Target Detection in 3D Radar Data Using Point Cloud Based Deep Neural Network,” ICPR, 2021.