Surface Detection for Sketched Single Photon Lidar

TL;DR

This paper introduces a memory-efficient surface detection algorithm for sketched single-photon lidar data that accurately identifies objects and estimates their depth and intensity with reduced computational and memory requirements.

Contribution

The paper presents a novel detection algorithm that operates solely on a small sketch, enabling fast, accurate, and memory-efficient lidar scene analysis.

Findings

The algorithm effectively detects surfaces using minimal sketch data.

It accurately estimates depth and intensity of objects from the sketch.

Experiments confirm its efficiency on synthetic and real datasets.

Abstract

Single-photon lidar devices are able to collect an ever-increasing amount of time-stamped photons in small time periods due to increasingly larger arrays, generating a memory and computational bottleneck on the data processing side. Recently, a sketching technique was introduced to overcome this bottleneck which compresses the amount of information to be stored and processed. The size of the sketch scales with the number of underlying parameters of the time delay distribution and not, fundamentally, with either the number of detected photons or the time-stamp resolution. In this paper, we propose a detection algorithm based solely on a small sketch that determines if there are surfaces or objects in the scene or not. If a surface is detected, the depth and intensity of a single object can be computed in closed-form directly from the sketch. The computational load of the proposed detection algorithm depends solely on the size of the sketch, in contrast to previous algorithms that depend at least linearly in the number of collected photons or histogram bins, paving the way for fast, accurate and memory efficient lidar estimation. Our experiments demonstrate the memory and statistical efficiency of the proposed algorithm both on synthetic and real lidar datasets.