The DLVS3-HST dataset is a large-scale, physically-based synthetic image collection designed to advance deep learning and computer vision research in satellite pose estimation. It features over 640,000 high-resolution images of the Hubble Space Telescope (HST), each rendered with accurate lighting, realistic material properties, and comprehensive ground-truth annotations. The dataset includes precise 6-DoF pose information, 37 keypoint locations per image, and additional pixel-level data such as segmentation masks, depth maps, and surface normals. DLVS3 was developed to address the scarcity of real-world annotated space imagery and to support the development and benchmarking of robust, generalizable algorithms for autonomous spacecraft operations, rendezvous, servicing, and debris removal. The dataset leverages advanced rendering pipelines and domain randomization to ensure diversity and realism, hopefully making it a foundational resource for both academic research and industrial applications in vision-based navigation. A small portion of the dataset is available here for direct download. It contains 100 multilayer 16-bit floating HDR EXR and 10,000 post-processed panchromatic PNG images. For access to the complete dataset (approx. 3.2 TB), please read the licence and register at:https://mi.services/dlvs3-hst-dataset-access/