Working Student (all genders) – Robust Feed-Forward 3D Reconstruction for Dynamic Scene

Xitaso · Augsburgjunior

En corto

▸Desarrollar modelos de reconstrucción 3D feed-forward para escenas dinámicas usando imágenes o videos.
▸Investigar robustez ante occlusiones, cambios de iluminación y mal tiempo en escenas en movimiento.
▸Trabajar con modelos preentrenados para tareas de ocupación 3D y 4D, con enfoque en IA para conducción autónoma y robótica.

At least C1 level

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¿Qué piden?

✓Estudiante de Ciencia de la Computación, IA, Robótica, Ingeniería Eléctrica, Ciencia de Datos o área relacionada.
✓Excelentes habilidades en programación con Python.
✓Experiencia práctica con PyTorch.
✓Conocimiento sólido en visión por computadora, aprendizaje profundo o geometría 3D.
✓Experiencia con estimación de profundidad, flujo óptico, nubes de puntos o reconstrucción 3D.
✓Nivel C1 de inglés para trabajo en entorno técnico.

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PythonPyTorch3D reconstructiondeep learningcomputer visionmulti-view visiondepth estimationoptical flowpoint cloudscamera pose estimation

¿A quién escribirle en Xitaso?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

Abstract Feed-forward 3D reconstruction models can recover scene geometry directly from images or videos without costly scene-specific optimization. By combining large-scale pre-training, multi-view reasoning, and strong geometric priors, these models provide an efficient alternative to traditional reconstruction pipelines such as Structure-from-Motion, NeRF, and optimization-based 3D Gaussian Splatting. Despite recent progress, current models remain sensitive to challenging real-world conditions. Occlusions, moving objects, illumination changes, nighttime scenes, reflections, rain, fog, and snow can result in incomplete geometry, unreliable correspondences, and temporally inconsistent predictions. Improving robustness under such conditions is essential for autonomous driving and robotic perception. As a working student, you will support the development of robust feed-forward reconstruction models for dynamic scenes. You will investigate methods for handling occlusion, changing illumination, and adverse weather, and explore how large reconstruction models can serve as general-purpose geometric backbones for downstream 3D scene understanding, particularly semantic occupancy prediction and 4D occupancy forecasting. These tasks interest you Develop and evaluate feed-forward 3D reconstruction models for dynamic scenes using monocular or multi-view image sequences. Investigate reconstruction robustness under partial and long-term occlusions, moving objects, and incomplete observations. Develop methods to improve geometric consistency under illumination changes, low-light conditions, shadows, and reflections. Evaluate and improve model performance under adverse weather conditions such as rain, fog, snow, and reduced visibility. Compare the developed methods with relevant baselines and document technical and experimental results. That makes you stand out You are currently pursuing a degree in computer science, artificial intelligence, robotics, electrical engineering, data science, or a related field. You have excellent programming skills in Python as well as hands-on experience with PyTorch . You have a good understanding of computer vision, deep learning, 3D geometry , or multi-view vision . Experience with depth estimation, optical flow, point clouds, camera pose estimation, NeRF, 3D Gaussian Splatting, or 3D reconstruction is highly beneficial. Your language skills enable you to perform your role in English (at least C1 level) . Knowledge of German is desirable but not required. Salary information Within our standardized and transparent salary framework, the pay for this position ranges from €15.50 to €19.50 per hour and is based on various factors, such as qualifications and experience.

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