Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation

TL;DR

This paper explores whether full-waveform flash lidar data can be used to identify material types, demonstrating potential improvements in semantic segmentation and comparing classifiers like random forest and TCN.

Contribution

It introduces the concept of using waveform shape for material classification and evaluates two classifiers, showing the potential and limitations of material identification from lidar data.

Findings

Material types can sometimes be distinguished from waveform data

Temporal convolutional neural networks outperform random forests in classification

Factors like angle and material similarity affect classification accuracy

Abstract

Advances in lidar technology have made the collection of 3D point clouds fast and easy. While most lidar sensors return per-point intensity (or reflectance) values along with range measurements, flash lidar sensors are able to provide information about the shape of the return pulse. The shape of the return waveform is affected by many factors, including the distance that the light pulse travels and the angle of incidence with a surface. Importantly, the shape of the return waveform also depends on the material properties of the reflecting surface. In this paper, we investigate whether the material type or class can be determined from the full-waveform response. First, as a proof of concept, we demonstrate that the extra information about material class, if known accurately, can improve performance on scene understanding tasks such as semantic segmentation. Next, we learn two different full-waveform material classifiers: a random forest classifier and a temporal convolutional neural network (TCN) classifier. We find that, in some cases, material types can be distinguished, and that the TCN generally performs better across a wider range of materials. However, factors such as angle of incidence, material colour, and material similarity may hinder overall performance.