Research Scientist - 3D Reconstruction (SfM & SLAM)

The role

You will advance methods to recover camera poses and geometry from real-world imagery. This involves working with classical multi-view geometry and state-of-the-art learned reconstructors.

What you will do

• Design camera pose estimators and 3D reconstructors.
• Build robust SfM and camera tracking pipelines for a variety of input imaging sensors.
• Develop bundle adjusters and nonlinear optimizers, including non-perspective camera formulations.
• Integrate and extend SOTA feed-forward reconstructors (VGGT, DA3, Pi3).
• Advance deep multi-view stereo, learned matching, and monocular depth methods for dense geometry.
• Build evaluation metrics for pose accuracy and reconstruction quality.
• Scale reconstruction methods to large, diverse real-world datasets.
• Collaborate with researchers to bring reconstruction advances into production systems.

Required qualifications

• PhD in computer vision with a research focus on 3D reconstruction.
• Publications at top venues (CVPR, ICCV, ECCV, NeurIPS).
• Deep understanding of multi-view geometry: camera models, epipolar geometry, triangulation, PnP, etc.
• Strong familiarity with SOTA deep reconstructors (VGGT, DA3, Pi3) and related areas such as deep MVS, learned matching, and monocular depth estimation.
• Hands-on experience with SfM/SLAM systems (COLMAP, ORB-SLAM) and nonlinear least-squares solvers (Ceres).
• Strong Python and PyTorch skills.
• Comfortable debugging failure cases on challenging real-world data.

Nice to have

• Experience shipping production-grade 3D reconstruction systems

About the company

SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and the simulation of reality.