Event Guided Depth Sensing

TL;DR

This paper introduces a bio-inspired, event-camera-driven depth sensing method that dynamically illuminates scene regions based on activity, significantly reducing power consumption while maintaining effective depth estimation in natural scenes.

Contribution

The paper presents a novel event-based structured light system that adaptively illuminates scene areas, improving efficiency and reducing power use compared to traditional uniform sampling methods.

Findings

Requires only 10% of the scene to be scanned in natural scenes.

Potential for 90% reduction in power consumption.

Feasibility demonstrated in simulated and real indoor sequences.

Abstract

Active depth sensors like structured light, lidar, and time-of-flight systems sample the depth of the entire scene uniformly at a fixed scan rate. This leads to limited spatio-temporal resolution where redundant static information is over-sampled and precious motion information might be under-sampled. In this paper, we present an efficient bio-inspired event-camera-driven depth estimation algorithm. In our approach, we dynamically illuminate areas of interest densely, depending on the scene activity detected by the event camera, and sparsely illuminate areas in the field of view with no motion. The depth estimation is achieved by an event-based structured light system consisting of a laser point projector coupled with a second event-based sensor tuned to detect the reflection of the laser from the scene. We show the feasibility of our approach in a simulated autonomous driving scenario and real indoor sequences using our prototype. We show that, in natural scenes like autonomous driving and indoor environments, moving edges correspond to less than 10% of the scene on average. Thus our setup requires the sensor to scan only 10% of the scene, which could lead to almost 90% less power consumption by the illumination source. While we present the evaluation and proof-of-concept for an event-based structured-light system, the ideas presented here are applicable for a wide range of depth-sensing modalities like LIDAR, time-of-flight, and standard stereo. Video is available at \url{https://youtu.be/Rvv9IQLYjCQ}.