# RAFT -- A Domain Adaptation Framework for RGB & LiDAR Semantic Segmentation Source: [RAFT -- A Domain Adaptation Framework for RGB & LiDAR Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-11-14T05:00:58.442Z Authors: Edward Humes, Xiaomin Lin, Boxun Hu, Rithvik Jonna, Tinoosh Mohsenin Published: 2025-05-07T16:02:46.000Z ## Abstract Image segmentation is a powerful computer vision technique for scene understanding. However, real-world deployment is stymied by the need for high-quality, meticulously labeled datasets. Synthetic data provides high-quality labels while reducing the need for manual data collection and annotation. However, deep neural networks trained on synthetic data often face the Syn2Real problem, leading to poor performance in real-world deployments. To mitigate the aforementioned gap in image segmentation, we propose RAFT, a novel framework for adapting image segmentation models using minimal labeled real-world data through data and feature augmentations, as well as active learning. To validate RAFT, we perform experiments on the synthetic-to-real "SYNTHIA-\>Cityscapes" and "GTAV-\>Cityscapes" benchmarks. We managed to surpass the previous state of the art, HALO. SYNTHIA-\>Cityscapes experiences an improvement in mIoU\* upon domain adaptation of 2.1%/79.9%, and GTAV-\>Cityscapes experiences a 0.4%/78.2% improvement in mIoU. Furthermore, we test our approach on the real-to-real benchmark of "Cityscapes-\>ACDC", and again surpass HALO, with a gain in mIoU upon adaptation of 1.3%/73.2%. Finally, we examine the effect of the allocated annotation budget and various components of RAFT upon the final transfer mIoU. ## 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]()