Open-vocabulary 3D scene perception in industrial environments

Autonomous 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.

Subjects

factory

industrial systems

manufacturing plant

segmentation

DDC Class

006.3: Artificial Intelligence

681: Precision Instruments and Other Devices

620: Engineering

Lizenz

https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication version

publishedVersion

Name

1-s2.0-S2212827126012126-main.pdf

Type

Main Article

Size

4.01 MB

Format

Adobe PDF

Options

Open-vocabulary 3D scene perception in industrial environments