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Kai Xu

4 papers hereh-index 9273 citations12 works total

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

author position
  • first author1
  • last author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.GR1
  • eess.IV1
same name
  • Kai Xu — 57 papers, h 49
  • Kai Xu — 48 papers, h 24
  • Kai Xu — 25 papers, h 9
  • Kai Xu — 19 papers, h 6
  • Kai Xu — 15 papers, h 7
  • Kai Xu — 15 papers, h 6

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

most citedStage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

eess.IV2023★ 1 cited

Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction

Kai Xu, Shiyu Lu, Bin Huang +2

Diffusion models have emerged as potential tools to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods. Neverth…

cs.GR2023

Tensorformer: Normalized Matrix Attention Transformer for High-quality Point Cloud Reconstruction

Hui Tian, Zheng Qin, Renjiao Yi +2

Surface reconstruction from raw point clouds has been studied for decades in the computer graphics community, which is highly demanded by modeling and rendering applications nowada…

cs.CV2023

Weakly-supervised Single-view Image Relighting

Renjiao Yi, Chenyang Zhu, Kai Xu

We present a learning-based approach to relight a single image of Lambertian and low-frequency specular objects. Our method enables inserting objects from photographs into new scen…

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…

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