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Chenyang Zhu

37 papers hereh-index 191.2k citations64 works total

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

author position
  • first author2
  • middle author31
  • last author2

Across the 35 of 37 papers where every author was matched, so the position is known.

fields
  • cs.CV24
  • cs.GR5
  • cs.RO4
  • cs.AI2
  • cs.LG1
  • eess.IV1
same name
  • Chenyang Zhu — 16 papers, h 3
  • Chenyang Zhu — 7 papers, h 5
  • Chenyang Zhu — 3 papers, h 4
  • Chenyang Zhu — 1 paper, h 1
  • Chenyang Zhu — 1 paper, h 1
  • Chenyang Zhu — 1 paper, h 10

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
20182026
most citedROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion

217 citations · 307 across the 36 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CV2022

Multi-resolution Monocular Depth Map Fusion by Self-supervised Gradient-based Composition

Yaqiao Dai, Renjiao Yi, Chenyang Zhu +2

Monocular depth estimation is a challenging problem on which deep neural networks have demonstrated great potential. However, depth maps predicted by existing deep models usually l…

cs.CV2022★ 1 cited

6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features

Chenyi Liu, Fei Chen, Lu Deng +5

The point pair feature (PPF) is widely used for 6D pose estimation. In this paper, we propose an efficient 6D pose estimation method based on the PPF framework. We introduce a well…

cs.CV2022★ 1 cited

DisARM: Displacement Aware Relation Module for 3D Detection

Yao Duan, Chenyang Zhu, Yuqing Lan +3

We introduce Displacement Aware Relation Module (DisARM), a novel neural network module for enhancing the performance of 3D object detection in point cloud scenes. The core idea of…

cs.CV2022

ARM3D: Attention-based relation module for indoor 3D object detection

Yuqing Lan, Yao Duan, Chenyi Liu +4

Relation context has been proved to be useful for many challenging vision tasks. In the field of 3D object detection, previous methods have been taking the advantage of context enc…

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