Working Student (all genders) – Robust Feed-Forward 3D Reconstruction for Dynamic Scene
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Position Overview & Specifications
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.
Your contact person
Daniela
+49 821 885882-0
Candidate Selection & Onboarding Process
Application & Resume Screening
Submit your tailored CV/Resume directly to the talent acquisition portal.
Technical & Competency Interviews
Virtual interviews with the hiring manager and multidisciplinary team.
Formal Offer & Benefits Negotiation
Written agreement outlining compensation, equity, retirement vesting, and relocation allowances.
Onboarding & Corporate Integration
Equipment provisioning, team orientation, and commencement of duties.
United Kingdom Right to Work & Skilled Worker Visa Guide
Employment in the United Kingdom requires legal Right to Work verified under the Home Office Points-Based Immigration System:
Sponsoring employers must hold an active Home Office A-rated Sponsor License and assign a valid Certificate of Sponsorship (CoS). Role must meet the general minimum salary threshold (£38,700) or occupation going rate.
Continuous employment under Skilled Worker status establishes eligibility for Indefinite Leave to Remain (ILR) after 5 continuous years, leading to British Citizenship.
Candidate Preparation Blueprint: General
Based on transatlantic hiring benchmarks for Working Student (all genders) – Robust Feed-Forward 3D Reconstruction for Dynamic Scene roles across Xitaso's corporate sector, successful applicants typically excel across three core dimensions:
Demonstrated portfolio evidence, architecture/system design case studies, or validated professional certifications directly applicable to General.
STAR method competency responses highlighting cross-functional leadership, conflict resolution, and delivering measurable enterprise ROI under tight timelines.
Total compensation expectation aligned within the benchmarked Salary Disclosed on Application bracket, including retirement vesting and health parity.
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