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Siyuan Li

18 papers hereh-index 181.9k citations50 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author10
  • middle author7

Across the 17 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CV13
  • cs.LG3
  • cs.AI2
same name
  • Siyuan Li — 48 papers, h 29
  • Siyuan Li — 14 papers, h 2
  • Siyuan Li — 13 papers, h 5
  • Siyuan Li — 13 papers, h 5
  • Siyuan Li — 11 papers, h 8
  • Siyuan Li — 9 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152023
most citedLarge-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55

53 citations · 195 across the 17 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017★ 2 cited

Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs

Xinqing Guo, Zhang Chen, Siyuan Li +2

Human visual system relies on both binocular stereo cues and monocular focusness cues to gain effective 3D perception. In computer vision, the two problems are traditionally solved…

cs.CV2017★ 53 cited

Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55

Li Yi, Lin Shao, Manolis Savva +47

We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…

cs.AI2017★ 13 cited

An Optimal Online Method of Selecting Source Policies for Reinforcement Learning

Siyuan Li, Chongjie Zhang

Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source…

cs.CV2017★ 11 cited

A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching

Zhang Chen, Xinqing Guo, Siyuan Li +2

Depth from defocus (DfD) and stereo matching are two most studied passive depth sensing schemes. The techniques are essentially complementary: DfD can robustly handle repetitive te…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.