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Hao Su

35 papers hereh-index 359.4k citations56 works total

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

author position
  • sole author1
  • middle author11
  • last author23

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

fields
  • cs.CV25
  • cs.LG8
  • cs.GR2
same name
  • Hao Su — 19 papers, h 40
  • Hao Su — 18 papers, h 21
  • Hao Su — 13 papers, h 10
  • Hao Su — 12 papers, h 5
  • Hao Su — 8 papers, h 4
  • Hao Su — 6 papers, h 15

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
20182023
most citedExtending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers

18 citations · 85 across the 17 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CV2023

Strivec: Sparse Tri-Vector Radiance Fields

Quankai Gao, Qiangeng Xu, Hao Su +2

We propose Strivec, a novel neural representation that models a 3D scene as a radiance field with sparsely distributed and compactly factorized local tensor feature grids. Our appr…

cs.CV2023

NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Support

Xinyue Wei, Fanbo Xiang, Sai Bi +4

We present a method for generating high-quality watertight manifold meshes from multi-view input images. Existing volumetric rendering methods are robust in optimization but tend t…

cs.CV2023★ 3 cited

MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field

Kaizhi Yang, Xiaoshuai Zhang, Zhiao Huang +3

We present MovingParts, a NeRF-based method for dynamic scene reconstruction and part discovery. We consider motion as an important cue for identifying parts, that all particles on…

cs.CV2023

TensoIR: Tensorial Inverse Rendering

Haian Jin, Isabella Liu, Peijia Xu +6

We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields. Unlike previous works that use purely MLP-based neural fields, thus sufferin…

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