{
  "id": "2008.02191",
  "version": "v1",
  "published": "2020-08-05T15:38:18.000Z",
  "updated": "2020-08-05T15:38:18.000Z",
  "title": "Active Perception using Light Curtains for Autonomous Driving",
  "authors": [
    "Siddharth Ancha",
    "Yaadhav Raaj",
    "Peiyun Hu",
    "Srinivasa G. Narasimhan",
    "David Held"
  ],
  "comment": "Published at the European Conference on Computer Vision (ECCV), 2020",
  "categories": [
    "cs.CV",
    "cs.LG",
    "cs.RO"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-08-05T15:38:18.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "active perception",
      "placing uncertainty-guided light curtains",
      "autonomous driving",
      "3d point cloud detector",
      "real-world 3d sensors"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}