{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/senseshift6d-multimodal-rgb-d-benchmarking","title":"SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations","arxiv_id":"2507.05751","date":"2025-07-08","proceeding":null,"authors":["Yegyu Han","Taegyoon Yoon","Dayeon Woo","Sojeong Kim","Hyung-Sin Kim"],"abstract":"Recent advances on 6D object-pose estimation has achieved high performance on representative benchmarks such as LM-O, YCB-V, and T-Less. However, these datasets were captured under fixed illumination and camera settings, leaving the impact of real-world variations in illumination, exposure, gain or depth-sensor mode - and the potential of test-time sensor control to mitigate such variations - largely unexplored. To bridge this gap, we introduce SenseShift6D, the first RGB-D dataset that physically sweeps 13 RGB exposures, 9 RGB gains, auto-exposure, 4 depth-capture modes, and 5 illumination levels. For three common household objects (spray, pringles, and tincase), we acquire 101.9k RGB and 10k depth images, which can provide 1,380 unique sensor-lighting permutations per object pose. Experiments with state-of-the-art models on our dataset show that applying sensor control during test-time induces greater performance improvement over digital data augmentation, achieving performance comparable to or better than costly increases in real-world training data quantity and diversity. Adapting either RGB or depth sensors individually is effective, while jointly adapting multimodal RGB-D configurations yields even greater improvements. SenseShift6D extends the 6D-pose evaluation paradigm from data-centered to sensor-aware robustness, laying a foundation for adaptive, self-tuning perception systems capable of operating robustly in uncertain real-world environments. Our dataset is available at: huggingface.co/datasets/Yegyu/SenseShift6D Associated scripts can be found at: github.com/yegyu-han/SenseShift6D","url_abs":"https://arxiv.org/abs/2507.05751v1","url_pdf":"https://arxiv.org/pdf/2507.05751v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"senseshift6d-multimodal-rgb-d-benchmarking","repo_url":"https://github.com/yegyu-han/senseshift6d","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"gone","observed_at":"2026-09-16","how":"tree_404+repo_404"}}],"tasks":[{"task_slug":"6d-pose-estimation-1","task_name":"6D Pose Estimation"},{"task_slug":"6d-pose-estimation","task_name":"6D Pose Estimation using RGB"},{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2507.05751","atlas_url":"https://app.syntology.ai/?focus=2507.05751","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}