DPDist: Comparing Point Clouds Using Deep Point Cloud Distance

Dahlia Urbach, Yizhak Ben-Shabat, Michael Lindenbaum

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

ملخص

We introduce a new deep learning method for point cloud comparison. Our approach, named Deep Point Cloud Distance (DPDist), measures the distance between the points in one cloud and the estimated surface from which the other point cloud is sampled. The surface is estimated locally using the 3D modified Fisher vector representation. The local representation reduces the complexity of the surface, enabling effective learning, which generalizes well between object categories. We test the proposed distance in challenging tasks, such as similar object comparison and registration, and show that it provides significant improvements over commonly used distances such as Chamfer distance, Earth mover’s distance, and others.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
الصفحات545-560
عدد الصفحات16
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2020
الحدث16th European Conference on Computer Vision, ECCV 2020 - Glasgow, بريطانيا
المدة: ٢٣ أغسطس ٢٠٢٠٢٨ أغسطس ٢٠٢٠

سلسلة المنشورات

الاسمLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ناشرSpringer Verlag

!!Conference

!!Conference16th European Conference on Computer Vision, ECCV 2020
الدولة/الإقليمبريطانيا
المدينةGlasgow
المدة٢٣/٠٨/٢٠٢٨/٠٨/٢٠

All Science Journal Classification (ASJC) codes

  • !!Theoretical Computer Science
  • !!General Computer Science

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