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researcher

Daniel Wang

9 papers hereh-index 5240 citations14 works total

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

author position
  • first author2
  • middle author7

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV6
  • cs.GR1
  • cs.LG1
  • q-bio.NC1
same name
  • Daniel Wang — 6 papers, h 6
  • Daniel Wang — 3 papers, h 4
  • Daniel Wang — 2 papers, h 3
  • Daniel Wang — 1 paper, h 2
  • Daniel Wang — 1 paper, h 3
  • Daniel Wang — 1 paper, h 2

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
20222025
most citedWorDepth: Variational Language Prior for Monocular Depth Estimation

2 citations · 4 across the 9 of their papers we have counts for

collaborators
Showing 2024 · cs.CVShow all

4 papers · 2 filters

cs.CV2024

RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions

Ziyao Zeng, Yangchao Wu, Hyoungseob Park +6

We propose a method for metric-scale monocular depth estimation. Inferring depth from a single image is an ill-posed problem due to the loss of scale from perspective projection du…

cs.CV2024

Iris: Integrating Language into Diffusion-based Monocular Depth Estimation

Ziyao Zeng, Jingcheng Ni, Daniel Wang +5

Traditional monocular depth estimation suffers from inherent ambiguity and visual nuisances. We demonstrate that language can enhance monocular depth estimation by providing an add…

cs.CV2024★ 2 cited

WorDepth: Variational Language Prior for Monocular Depth Estimation

Ziyao Zeng, Daniel Wang, Fengyu Yang +6

Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly…

cs.CV2024

Binding Touch to Everything: Learning Unified Multimodal Tactile Representations

Fengyu Yang, Chao Feng, Ziyang Chen +8

The ability to associate touch with other modalities has huge implications for humans and computational systems. However, multimodal learning with touch remains challenging due to…

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