Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning

TL;DR

This paper introduces a hybrid domain adaptation framework for LiDAR semantic segmentation that combines GAN-based translation, alternating skip connections, and multiple learning paradigms to improve performance across different domains.

Contribution

It proposes a novel domain adaptation method using a GAN with alternating skip connections integrated with semi-supervised, self-supervised, and unsupervised learning techniques.

Findings

Achieves superior performance on benchmark datasets.

Effectively mitigates domain shift in LiDAR segmentation.

Outperforms existing baseline methods.

Abstract

In this paper we address the challenging problem of domain adaptation in LiDAR semantic segmentation. We consider the setting where we have a fully-labeled data set from source domain and a target domain with a few labeled and many unlabeled examples. We propose a domain adaption framework that mitigates the issue of domain shift and produces appealing performance on the target domain. To this end, we develop a GAN-based image-to-image translation engine that has generators with alternating connections, and couple it with a state-of-the-art LiDAR semantic segmentation network. Our framework is hybrid in nature in the sense that our model learning is composed of self-supervision, semi-supervision and unsupervised learning. Extensive experiments on benchmark LiDAR semantic segmentation data sets demonstrate that our method achieves superior performance in comparison to strong baselines and prior arts.