mlsanityData to 14 Oct 2026

Objects Can Move: 3D Change Detection by Geometric Transformation Constistency

arXiv 2208.09870 ↗First posted 21 Aug 2022cs.CVcited by 13

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Abstract (arXiv)

AR/VR applications and robots need to know when the scene has changed. An example is when objects are moved, added, or removed from the scene. We propose a 3D object discovery method that is based only on scene changes. Our method does not need to encode any assumptions about what is an object, but rather discovers objects by exploiting their coherent move. Changes are initially detected as differences in the depth maps and segmented as objects if they undergo rigid motions. A graph cut optimization propagates the changing labels to geometrically consistent regions. Experiments show that our method achieves state-of-the-art performance on the 3RScan dataset against competitive baselines. The source code of our method can be found at https://github.com/katadam/ObjectsCanMove.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 11 of its 41 references that are in the map. First posted on arXiv 21 Aug 2022.

Builds on

11 of 41 references.

References · 11

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2017Mask R-CNNcited by 7,277
  3. 2017Billion-scale similarity search with GPUscited by 2,497
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  5. 20163DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructionscited by 336
  6. 2019RIO: 3D Object Instance Re-Localization in Changing Indoor Environmentscited by 188
  7. 2021PointDSC: Robust Point Cloud Registration using Deep Spatial Consistencycited by 142
  8. 2018MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objectscited by 95
  9. 2020A Generalization of Otsu's Method and Minimum Error Thresholdingcited by 16
  10. 2019Rescan: Inductive Instance Segmentation for Indoor RGBD Scanscited by 12
  11. 2016Efficient 2D and 3D Facade Segmentation using Auto-Contextcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 6; 2026: 2.

13 papers.

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