# ShelfChange3D: Object-Level 3D Change Detection for Retail Shelf Monitoring

arXiv: https://arxiv.org/abs/2610.01283 · HTML: https://mlsanity.com/paper/2610.01283/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 49 references that are in the map.
- First posted on arXiv 1 Oct 2026.

## Builds on (most cited first, 30 of 30)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [An End-to-End Transformer Model for 3D Object Detection](https://mlsanity.com/paper/2109.08141/) (2021)
- [SAM 3D: 3Dfy Anything in Images](https://mlsanity.com/paper/2511.16624/) (2025)
- [RIO: 3D Object Instance Re-Localization in Changing Indoor Environments](https://mlsanity.com/paper/1908.06109/) (2019)
- [Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild](https://mlsanity.com/paper/2207.10660/) (2022)
- [H3DNet: 3D Object Detection Using Hybrid Geometric Primitives](https://mlsanity.com/paper/2006.05682/) (2020)
- [FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection](https://mlsanity.com/paper/2112.00322/) (2021)
- [Precise Detection in Densely Packed Scenes](https://mlsanity.com/paper/1904.00853/) (2019)
- [Weakly Supervised Silhouette-based Semantic Scene Change Detection](https://mlsanity.com/paper/1811.11985/) (2018)
- [Cubify Anything: Scaling Indoor 3D Object Detection](https://mlsanity.com/paper/2412.04458/) (2024)
- [Detect Anything 3D in the Wild](https://mlsanity.com/paper/2504.07958/) (2025)
- [DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection](https://mlsanity.com/paper/2103.00879/) (2021)
- [ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments](https://mlsanity.com/paper/2103.05368/) (2021)
- [How to Reduce Change Detection to Semantic Segmentation](https://mlsanity.com/paper/2206.07557/) (2022)
- [Open Vocabulary Monocular 3D Object Detection](https://mlsanity.com/paper/2411.16833/) (2024)
- [Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints](https://mlsanity.com/paper/1712.02941/) (2017)
- [Pixel-Aligned Recurrent Queries for Multi-View 3D Object Detection](https://mlsanity.com/paper/2310.01401/) (2023)
- [Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot](https://mlsanity.com/paper/2601.02078/) (2026)
- [Objects Can Move: 3D Change Detection by Geometric Transformation Constistency](https://mlsanity.com/paper/2208.09870/) (2022)
- [Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms](https://mlsanity.com/paper/2409.16850/) (2024)
- [Has Anything Changed? 3D Change Detection by 2D Segmentation Masks](https://mlsanity.com/paper/2312.01148/) (2023)
- [SceneDiff: A Benchmark and Method for Multiview Object Change Detection](https://mlsanity.com/paper/2512.16908/) (2025)
- [3D Scene Change Modeling With Consistent Multi-View Aggregation](https://mlsanity.com/paper/2512.22830/) (2025)
- [Rethinking the Localization in Weakly Supervised Object Localization](https://mlsanity.com/paper/2308.06161/) (2023)

## Abstract (arXiv)

Reliable shelf monitoring is an important capability for retail automation, yet existing out-of-stock detection methods mainly operate in image space and lack metric 3D localization for downstream robotic systems. We formulate shelf monitoring as object-level 3D change detection: given two RGB-D observations captured at different times, the goal is to identify changed products and localize each change with a 3D bounding box. To support this task, we introduce ShelfChange3D, comprising 145K synthetic and 5K real-world paired RGB-D observations with object-level 3D change annotations. We further propose ChangeBox, an end-to-end framework that jointly reasons over paired observations and predicts object-level 3D change boxes. To improve localization accuracy, we introduce a geometry-based refinement stage that exploits depth and gravity prior to estimate relative pose and refine predicted boxes. Experiments show that ChangeBox outperforms existing change detection baselines, with further gains from refinement and effective transfer from synthetic to real-world observations.