The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 177–182, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-177-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 177–182, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-177-2020

  12 Aug 2020

12 Aug 2020

INDOOR SCENE REGISTRATION BASED ON KEY POINTS SAMPLING AND HIERARCHICAL FEATURE LEARNING

M. Ai1,2, C. Liu1, H. Shen2, and F. Cheng1 M. Ai et al.
  • 1College of Surveying and Geo-Informatics, Tongji Unversity, 200092 Shanghai, China
  • 2Information Sciences and Technology, The Pennsylvania State University, State College, USA

Keywords: Indoor Scene, LiDAR Mapping, Point Cloud Registration, PointNet, Hierarchical Feature Descriptor

Abstract. PointNet has been widely considered as a popular representation for unstructured point clouds with the aim of classification and segmentation. To date, recent researches represent the limitation of the PointNet to pose estimation and alignment of real environment, due to the low performance in pattern learning to complex scenes. This paper presents an end-to-end deep learning method for point clouds registration of indoor environment. The proposed method involves three steps. Firstly, feature pre-processing extracts the key-points by adaptive Harris 3D algorithm and generate the local group by point grouping. Second, hierarchical feature learning network is trained to describe the local group as feature descriptors. Finally, loss function between feature descriptor is trained. The key contribution is that we innovatively use the key-points to generate multi-layer feature vector, which can provide the contextual local features of the indoor environment. The results shows that our method achieves comparable registration accuracy to the present state-of-art geometric methods in the indoor environment. We comprehensively validate the accuracy of our approach using S3DIS dataset. The high accuracy demonstrates that our method can be used in point clouds registration accurately.