CF-SIS: Semantic-Instance Segmentation of 3D Point Clouds by Context Fusion with Self-Attention

Author:

Wen Xin1,Han Zhizhong2,Youk Geunhyuk1,Liu Yu-Shen1

Affiliation:

1. Tsinghua University, Beijing, China

2. University of Maryland, College Park, College Park, MD, USA

Funder

National Key R&D Program of China

Publisher

ACM

Reference42 articles.

1. Iro Armeni Sasha Sax Amir R Zamir and Silvio Savarese. 2017. Joint 2D-3D-semantic data for indoor scene understanding. arXiv preprint arXiv:1702.01105 (2017). Iro Armeni Sasha Sax Amir R Zamir and Silvio Savarese. 2017. Joint 2D-3D-semantic data for indoor scene understanding. arXiv preprint arXiv:1702.01105 (2017).

2. Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views

3. 3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN With Hierarchical Attention Aggregation

4. View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions

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2. JSMNET: IMPROVING INDOOR POINT CLOUD SEMANTIC AND INSTANCE SEGMENTATION THROUGH SELF-ATTENTION AND MULTISCALE FUSION;The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences;2023-12-13

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