# Active Perception using Light Curtains for Autonomous Driving

arXiv: https://arxiv.org/abs/2008.02191 · HTML: https://mlsanity.com/paper/2008.02191/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Synthetic Datasets for Autonomous Driving: A Survey](https://mlsanity.com/paper/2304.12205/) (2023)
- [Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving](https://mlsanity.com/paper/2105.00373/) (2021)
- [Active Safety Envelopes using Light Curtains with Probabilistic Guarantees](https://mlsanity.com/paper/2107.04000/) (2021)
- [Structured Light with Redundancy Codes](https://mlsanity.com/paper/2206.09243/) (2022)
- [Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits](https://mlsanity.com/paper/2302.12597/) (2023)
- [CARE: Camera-Residual Reserves for First Sightings in Adaptive LiDAR Sensing](https://mlsanity.com/paper/2608.24282/) (2026)

## Abstract (arXiv)

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.