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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.05751 |
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| _version_ | 1866915873094107136 |
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| author | Han, Yegyu Yoon, Taegyoon Woo, Dayeon Kim, Sojeong Kim, Hyung-Sin |
| author_facet | Han, Yegyu Yoon, Taegyoon Woo, Dayeon Kim, Sojeong Kim, Hyung-Sin |
| contents | Recent advances on 6D object pose estimation have 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 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 six common household objects, we acquire 198.8k RGB and 20.0k depth images (i.e., 795.4k RGB-D scenes), providing 1,380 unique sensor-lighting permutations per object pose. Experiments with state-of-the-art pretrained, generalizable pose estimators reveal substantial performance variation across lighting and sensor settings, despite their large-scale pretraining. Strikingly, even instance-level estimators-trained and tested on identical objects and backgrounds-exhibit pronounced sensitivity to environmental and sensor shifts. These findings establish sensor- and environment-aware robustness as an underexplored yet essential dimension for real-world deployment, and motivate SenseShift6D as a necessary benchmark for the community. Finally, to illustrate the opportunity enabled by this benchmark, we evaluate test-time multimodal sensor selection without retraining. An idealized (oracle) controller yields remarkable gains of up to +16.7 pp for generalizable models, whereas a practical consistency-based proxy improves performance only marginally, highlighting substantial headroom and the need for future research on reliable sensor-aware adaptation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05751 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations Han, Yegyu Yoon, Taegyoon Woo, Dayeon Kim, Sojeong Kim, Hyung-Sin Computer Vision and Pattern Recognition Recent advances on 6D object pose estimation have 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 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 six common household objects, we acquire 198.8k RGB and 20.0k depth images (i.e., 795.4k RGB-D scenes), providing 1,380 unique sensor-lighting permutations per object pose. Experiments with state-of-the-art pretrained, generalizable pose estimators reveal substantial performance variation across lighting and sensor settings, despite their large-scale pretraining. Strikingly, even instance-level estimators-trained and tested on identical objects and backgrounds-exhibit pronounced sensitivity to environmental and sensor shifts. These findings establish sensor- and environment-aware robustness as an underexplored yet essential dimension for real-world deployment, and motivate SenseShift6D as a necessary benchmark for the community. Finally, to illustrate the opportunity enabled by this benchmark, we evaluate test-time multimodal sensor selection without retraining. An idealized (oracle) controller yields remarkable gains of up to +16.7 pp for generalizable models, whereas a practical consistency-based proxy improves performance only marginally, highlighting substantial headroom and the need for future research on reliable sensor-aware adaptation. |
| title | SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.05751 |