SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations

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