Project SimNet

We assembled a team of researchers from UC-Berkeley, Google, Amazon and MIT, to ask the question: Can we drastically reduce the cost of data annotation? In response, we have developed SimNet, a low-quality procedural simulator for tackling 3D perception for home robotics.

To enable this we first built a procedural home simulator, that can generate a wide range of home layouts, furniture and clutter.

Given synthetic 3D data, we can get annotations for room-level segmenation, OBBs of objects and the state of articulated objects. To enable transfer to the real world, we use our novel stereo-based sim2real technique. Below are predictions from our panoptic detector trained only on synthetic data.