Incremental class discovery for semantic segmentation with rgbd sensing

Yoshikatsu Nakajima, Byeongkeun Kang, Hideo Saito, Kris Kitani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)


This work addresses the task of open world semantic segmentation using RGBD sensing to discover new semantic classes over time. Although there are many types of objects in the real-word, current semantic segmentation methods make a closed world assumption and are trained only to segment a limited number of object classes. Towards a more open world approach, we propose a novel method that incrementally learns new classes for image segmentation. The proposed system first segments each RGBD frame using both color and geometric information, and then aggregates that information to build a single segmented dense 3D map of the environment. The segmented 3D map representation is a key component of our approach as it is used to discover new object classes by identifying coherent regions in the 3D map that have no semantic label. The use of coherent region in the 3D map as a primitive element, rather than traditional elements such as surfels or voxels, also significantly reduces the computational complexity and memory use of our method. It thus leads to semi-real-time performance at 10.7 Hz when incrementally updating the dense 3D map at every frame. Through experiments on the NYUDv2 dataset, we demonstrate that the proposed method is able to correctly cluster objects of both known and unseen classes. We also show the quantitative comparison with the state-of-the-art supervised methods, the processing time of each step, and the influences of each component.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Computer Vision, ICCV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781728148038
Publication statusPublished - 2019 Oct
Event17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of
Duration: 2019 Oct 272019 Nov 2

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499


Conference17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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