  This paper introduces a pipeline to parametrically sample and render
multi-task vision datasets from comprehensive 3D scans from the real world.
Changing the sampling parameters allows one to "steer" the generated datasets
to emphasize specific information. In addition to enabling interesting lines of
research, we show the tooling and generated data suffice to train robust vision
models.
  Common architectures trained on a generated starter dataset reached
state-of-the-art performance on multiple common vision tasks and benchmarks,
despite having seen no benchmark or non-pipeline data. The depth estimation
network outperforms MiDaS and the surface normal estimation network is the
first to achieve human-level performance for in-the-wild surface normal
estimation -- at least according to one metric on the OASIS benchmark.
  The Dockerized pipeline with CLI, the (mostly python) code, PyTorch
dataloaders for the generated data, the generated starter dataset, download
scripts and other utilities are available through our project website,
https://omnidata.vision.
