Most real-world 3D sensors such as LiDARs perform fixed scans of the entire environment, while being decoupled from the recognition system that processes the sensor data. In this work, we propose a method for 3D object recognition using programmable light curtains, a resource-efficient controllable sensor that measures depth at user-specified locations in the environment. Crucially, we propose using prediction uncertainty of a deep learning based 3D point cloud detector to guide active perception. Given a neural network's uncertainty, we derive an optimization objective to place light curtains using the principle of maximizing information gain. Then, we develop a novel and efficient optimization algorithm to maximize this objective by encoding the physical constraints of the device into a constraint graph and optimizing with dynamic programming. We show how a 3D detector can be trained to detect objects in a scene by sequentially placing uncertainty-guided light curtains to successively improve detection accuracy.

@InProceedings{Ancha_2020_ECCV,
  author="Ancha, Siddharth
  and Raaj, Yaadhav
  and Hu, Peiyun
  and Narasimhan, Srinivasa G.
  and Held, David",
  editor="Vedaldi, Andrea
  and Bischof, Horst
  and Brox, Thomas
  and Frahm, Jan-Michael",
  title="Active Perception Using Light Curtains for Autonomous Driving",
  booktitle="Computer Vision -- ECCV 2020",
  year="2020",
  publisher="Springer International Publishing",
  address="Cham",
  pages="751--766",
  isbn="978-3-030-58558-7"
}