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

arXiv: https://arxiv.org/abs/2507.05751 · HTML: https://mlsanity.com/paper/2507.05751/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 42 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 27 of 27)

- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items](https://mlsanity.com/paper/2204.11918/) (2022)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [BOP: Benchmark for 6D Object Pose Estimation](https://mlsanity.com/paper/1808.08319/) (2018)
- [T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects](https://mlsanity.com/paper/1701.05498/) (2017)
- [MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare](https://mlsanity.com/paper/2212.06870/) (2022)
- [GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimator](https://mlsanity.com/paper/2102.12145/) (2021)
- [SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation](https://mlsanity.com/paper/2311.15707/) (2023)
- [ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation](https://mlsanity.com/paper/2203.09418/) (2022)
- [GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence](https://mlsanity.com/paper/2311.14155/) (2023)
- [HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects](https://mlsanity.com/paper/1904.03167/) (2019)
- [6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark](https://mlsanity.com/paper/2203.05701/) (2022)
- [GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision](https://mlsanity.com/paper/2007.13124/) (2020)
- [Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains](https://mlsanity.com/paper/2404.15882/) (2024)
- [HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation](https://mlsanity.com/paper/2311.12588/) (2023)
- [6D Object Pose Tracking in Internet Videos for Robotic Manipulation](https://mlsanity.com/paper/2503.10307/) (2025)
- [Adaptive Camera Sensor for Vision Models](https://mlsanity.com/paper/2503.02170/) (2025)
- [HMD-EgoPose: Head-Mounted Display-Based Egocentric Marker-Less Tool and Hand Pose Estimation for Augmented Surgical Guidance](https://mlsanity.com/paper/2202.11891/) (2022)
- [Enhancing Video Analytics Accuracy via Real-time Automated Camera Parameter Tuning](https://mlsanity.com/paper/2107.03964/) (2021)
- [DoPose-6D dataset for object segmentation and 6D pose estimation](https://mlsanity.com/paper/2204.13613/) (2022)
- [Uncovering the Background-Induced bias in RGB based 6-DoF Object Pose Estimation](https://mlsanity.com/paper/2304.08230/) (2023)

## Built on by (most cited first, 3 of 3)

- [AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift](https://mlsanity.com/paper/2507.07820/) (2025)
- [EgoXtreme: A Dataset for Robust Object Pose Estimation in Egocentric Views under Extreme Conditions](https://mlsanity.com/paper/2603.25135/) (2026)
- [CLON: Cue-Calibrated Linguistic Object Onboarding for Zero-Shot 6D Pose Front-Ends](https://mlsanity.com/paper/2609.04784/) (2026)

## Abstract (arXiv)

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.