Physical Annotation for Automated Optical Inspection: A Concept for In-Situ, Pointer-Based Trainingdata Generation

This paper introduces a novel physical annotation system designed to generate training data for automated optical inspection. The system uses pointer-based in-situ interaction to transfer the valuable expertise of trained inspection personnel directly into a machine learning (ML) training pipeline. Unlike conventional screen-based annotation methods, our system captures physical trajectories and contours directly on the object, providing a more intuitive and efficient way to label data. The core technology uses calibrated, tracked pointers to accurately record user input and transform these spatial interactions into standardised annotation formats that are compatible with open-source annotation software. Additionally, a simple projector-based interface projects visual guidance onto the object to assist users during the annotation process, ensuring greater accuracy and consistency. The proposed concept bridges the gap between human expertise and automated data generation, enabling non-IT experts to contribute to the ML training pipeline and preventing the loss of valuable training samples. Preliminary evaluation results confirm the feasibility of capturing detailed annotation trajectories and demonstrate that integration with CVAT streamlines the workflow for subsequent ML tasks. This paper details the system architecture, calibration procedures and interface design, and discusses its potential contribution to future ML data generation for automated optical inspection.

Vanishing Depth: A Depth Adapter with Positional Depth Encoding for Generalized Image Encoders

Generalized metric depth understanding is critical for precise vision-guided robotics, which current state-of-the-art (SOTA) vision-encoders do not support. To address this, we propose Vanishing Depth, a self-supervised training approach that extends pretrained RGB encoders to incorporate and align metric depth into their feature embeddings. Based on our novel positional depth encoding, we enable stable depth density and depth distribution invariant feature extraction. We achieve performance improvements and SOTA results across a spectrum of relevant RGBD downstream tasks - without the necessity of finetuning the encoder. Most notably, we achieve 56.05 mIoU on SUN-RGBD segmentation, 88.3 RMSE on Void's depth completion, and 83.8 Top 1 accuracy on NYUv2 scene classification. In 6D-object pose estimation, we outperform our predecessors of DinoV2, EVA-02, and Omnivore and achieve SOTA results for non-finetuned encoders in several related RGBD downstream tasks.

On the Application of Egocentric Computer Vision to Industrial Scenarios

Egocentric vision aims to capture and analyse the world from the first-person perspective. We explore the possibilities for egocentric wearable devices to improve and enhance industrial use cases w.r.t. data collection, annotation, labelling and downstream applications. This would contribute to easier data collection and allow users to provide additional context. We envision that this approach could serve as a supplement to the traditional industrial Machine Vision workflow. Code, Dataset and related resources will be available at: https://github.com/Vivek9Chavan/EgoVis24