Moenck, KenoKenoMoenckFlorea, Adrian PhilipAdrian PhilipFloreaKoch, JulianJulianKochSchüppstuhl, ThorstenThorstenSchüppstuhl2026-10-232026-10-23202659th CIRP Conference on Manufacturing Systems, CIRP CMS 2026https://hdl.handle.net/11420/65426Autonomous vision applications in production, intralogistics, or manufacturing environments require perception capabilities beyond a small, fixed set of classes. Recent open-vocabulary methods, leveraging 2D Vision-Language Foundation Models (VLFMs), target this task but often rely on class-agnostic segmentation models pre-trained on non-industrial datasets (e.g., household scenes). In this work, we first demonstrate that such models fail to generalize, performing poorly on common industrial objects. Therefore, we propose a training-free, open-vocabulary 3D perception pipeline that overcomes this limitation. Instead of using a pre-trained model to generate instance proposals, our method simply generates masks by merging pre-computed superpoints based on their semantic features. Following, we evaluate the domain-adapted VLFM ”IndustrialCLIP” on a representative 3D industrial workshop scene for open-vocabulary querying. Our qualitative results demonstrate successful segmentation of industrial objects.en2212-82712026529534Elsevierhttps://creativecommons.org/licenses/by-nc-nd/4.0/factoryindustrial systemsmanufacturing plantsegmentationComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial IntelligenceTechnology::681: Precision Instruments and Other DevicesTechnology::620: EngineeringConference Paper10.1016/j.procir.2026.03.28910.15480/882.18993