Shenghua Gao

Computer Vision and Pattern Recognition · with Chen · 2024

A researcher in the Science corpus: 1 paper; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 34 citations into it
• Computer Graphics and Computer-Aided Design — 29 citations into it
• Computational Mechanics — 24 citations into it
• 3D Vision & Pose — 10 citations into it

Whom Shenghua Gao trusts

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

1. Andreas Geiger (in Computer Vision and Pattern Recognition)
2. Michael Oechsle (in Computer Vision and Pattern Recognition)
3. Jonathan T. Barron (in Computer Vision and Pattern Recognition)
4. Jan-Michael Frahm (in Computer Vision and Pattern Recognition)
5. Songyou Peng (in Computer Vision and Pattern Recognition)
6. Johannes L. Schönberger (in Computer Vision and Pattern Recognition)
7. Vladlen Koltun (in Computer Vision and Pattern Recognition)
8. Peter Hedman (in Computer Vision and Pattern Recognition)
9. Michael Niemeyer (in Computer Vision and Pattern Recognition)
10. Ben Mildenhall (in Computer Vision and Pattern Recognition)

Papers from Shenghua Gao's perspective

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

1. Screened poisson surface reconstruction
2. DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
3. Occupancy Networks: Learning 3D Reconstruction in Function Space (Computer Vision and Pattern Recognition)
4. Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision
5. Structure-from-Motion Revisited
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Large Scale Multi-view Stereopsis Evaluation (2014 IEEE Conference on Computer Vision and Pattern Recognition)
8. Instant neural graphics primitives with a multiresolution hash encoding
9. Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction (Computer Vision and Pattern Recognition)
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• 2D Gaussian Splatting for Geometrically Accurate Radiance Fields (2024) — 1,465 citations