  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 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. Code and details can be found on the project
webpage: http://siddancha.github.io/projects/active-perception-light-curtains.
