# POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes

arXiv: https://arxiv.org/abs/2205.01202 · HTML: https://mlsanity.com/paper/2205.01202/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2022.

## Built on by (most cited first, 9 of 9)

- [ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning](https://mlsanity.com/paper/2309.16650/) (2023)
- [SceneGPT: A Language Model for 3D Scene Understanding](https://mlsanity.com/paper/2408.06926/) (2024)
- [ObVi-SLAM: Long-Term Object-Visual SLAM](https://mlsanity.com/paper/2309.15268/) (2023)
- [SceneDiff: A Benchmark and Method for Multiview Object Change Detection](https://mlsanity.com/paper/2512.16908/) (2025)
- [Image-to-Lidar Relational Distillation for Autonomous Driving Data](https://mlsanity.com/paper/2409.00845/) (2024)
- [Gaussian Mapping for Evolving Scenes](https://mlsanity.com/paper/2506.06909/) (2025)
- [RoboAtlas: Contextual Active SLAM](https://mlsanity.com/paper/2606.26046/) (2026)
- [LT-Mem: Volatility-Aware Spatio-Temporal Memory for Lifelong Scene Understanding](https://mlsanity.com/paper/2608.19059/) (2026)
- [Open-Vocabulary Spatio-Temporal Scene Graph for Robot Perception and Teleoperation Planning](https://mlsanity.com/paper/2509.23107/) (2025)

## Abstract (arXiv)

Maintaining an up-to-date map to reflect recent changes in the scene is very important, particularly in situations involving repeated traversals by a robot operating in an environment over an extended period. Undetected changes may cause a deterioration in map quality, leading to poor localization, inefficient operations, and lost robots. Volumetric methods, such as truncated signed distance functions (TSDFs), have quickly gained traction due to their real-time production of a dense and detailed map, though map updating in scenes that change over time remains a challenge. We propose a framework that introduces a novel probabilistic object state representation to track object pose changes in semi-static scenes. The representation jointly models a stationarity score and a TSDF change measure for each object. A Bayesian update rule that incorporates both geometric and semantic information is derived to achieve consistent online map maintenance. To extensively evaluate our approach alongside the state-of-the-art, we release a novel real-world dataset in a warehouse environment. We also evaluate on the public ToyCar dataset. Our method outperforms state-of-the-art methods on the reconstruction quality of semi-static environments.