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Workspace

Workspace

Workspace

Intern, Geometric Vision Algorithms

Job Type

Workspace

Part Time

Remote

Teams

R&D

Salary (annual)

$12,000~$42,000

What You’ll Do

  1. Rapid SOTA Reproduction & Adaptation:

    1. Implement training configs, hyperparameters, and losses/constraints (photometric reprojection, geometric consistency, uncertainty calibration).

    2. On data, deliver train/val/test pipelines, A/B baselines, and reproducibility scripts; produce alignment and error reports (depth metrics, pose errors).

  2. Design Training Data & Gains :

    1. Design Training Dataset (sampling, rebalancing, synthesis, augmentation);

    2. implement pre/post-processing to improve metrics (distortion correction, color consistency, depth fusion and hole filling, temporal consistency and uncertainty integration).

Job Requirements

  1. Basic requirements:

    1. Camera models of Equirectangular/cubemap, stitching/slicing, exposure/color alignment, seam/geometric consistency, stitching depth map.

    2. Multi-view Geometry, triangulation/BA, depth fusion, uncertainty estimation; solid grasp of numerical optimization and linear algebra for vision.

    3. Attention/positional encodings, cross-view & temporal attention, cost-volume and epipolar-aware constraints, long-context memory;

    4. forward/reverse processes, noise schedules, conditional guidance, and sampling/distillation; with extensions or gains that preserve camera-model/epipolar consistency, scale observability, and uncertainty calibration.

  2. Training & Evaluation & Coding Experience:

    1. Proficient in Python (3.x)/PyTorch. Strong with NumPy/SciPy/OpenCV; able to deliver E2E training pipelines.

    2. Familiarity with multi-GPU/distributed training, dataloader performance, mixed precision; able to export ONNX/TorchScript and validate accuracy/latency.

    3. Solid C++ (C++17+) and libraries: Eigen/Ceres/g2o/OpenCV/Open3D (≥1).

    4. Familiar with compilation tools such as CMake/Ninja, vcpkg/Conan.

    5. Comfortable building/running/debugging in Docker (aligned with your duties on image size optimization and stability testing).

    6. Fluent with and able to lightly extend COLMAP / OpenMVG / OpenMVS (≥1).

Preferred

  • Publications/Open-source: CVPR/ICCV/ECCV/NeurIPS/ICLR or notable repos.

  • Model-level contributions (architecture/loss changes, domain transfer, sparse-supervision utilization). Compensation uplift possible.

  • Experienced in optimizing model inference efficiency: TensorRT / OpenVINO / TVM.

  • Candidates with more than one year of relevant work experience (including internship experience) will be given priority consideration.



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