# Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning Source: [Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:03:49.010Z Authors: Eduardo R. Corral-Soto, Mrigank Rochan, Yannis Y. He, Shubhra Aich, Yang Liu, Liu Bingbing Published: 2022-01-14T18:13:09.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()