Xiaogang Jin

Computer Vision and Pattern Recognition · with Zhang · 2009–2023

A researcher in the Science corpus: 2 papers; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Xiaogang Jin trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Jonathan T. Barron (in Computer Vision and Pattern Recognition)
  2. Peter Hedman (in Computer Vision and Pattern Recognition)
  3. Deva Ramanan (in Computer Vision and Pattern Recognition)
  4. Ben Mildenhall (in Computer Vision and Pattern Recognition)
  5. Andrew Zisserman (in Computer Vision and Pattern Recognition)
  6. Pratul P. Srinivasan (in Computer Vision and Pattern Recognition)
  7. Kaiming He (in Computer Vision and Pattern Recognition)
  8. Ross B. Girshick (in Computer Vision and Pattern Recognition)
  9. Jitendra Malik (in Computer Vision and Pattern Recognition)
  10. Justin Johnson (in Computer Vision and Pattern Recognition)

Papers from Xiaogang Jin's perspective

Ordered by how strongly the researchers Xiaogang Jin trusts endorse each paper. Same corpus, a different order for every researcher.

  1. PyTorch: An Imperative Style, High-Performance Deep Learning Library
  2. Neural Sparse Voxel Fields
  3. Instant neural graphics primitives with a multiresolution hash encoding
  4. Structure-from-Motion Revisited
  5. Space-time Neural Irradiance Fields for Free-Viewpoint Video (Computer Vision and Pattern Recognition)
  6. D-NeRF: Neural Radiance Fields for Dynamic Scenes (Computer Vision and Pattern Recognition)
  7. FastNeRF: High-Fidelity Neural Rendering at 200FPS (IEEE International Conference on Computer Vision)
  8. PlenOctrees for Real-time Rendering of Neural Radiance Fields (IEEE International Conference on Computer Vision)
  9. NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction (Neural Information Processing Systems)
  10. Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction (Computer Vision and Pattern Recognition)

Their papers