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Angela Dai

5 papers here

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

author position
  • middle author2
  • last author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.GR1
ORCID 0000-0002-6241-8782
same name
  • Angela Dai — 34 papers, h 43
  • Angela Dai — 6 papers, h 5
  • Angela Dai — 5 papers, h 3
  • Angela Dai — 4 papers, h 1
  • Angela Dai — 4 papers, h 5
  • Angela Dai — 3 papers

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 citedLayerBuilder: Layer Decomposition for Interactive Image and Video Color Editing

17 citations · 23 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023

ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes

Chandan Yeshwanth, Yueh-Cheng Liu, Matthias Nießner +1

We present ScanNet++, a large-scale dataset that couples together capture of high-quality and commodity-level geometry and color of indoor scenes. Each scene is captured with a hig…

cs.CV2023

Mesh2Tex: Generating Mesh Textures from Image Queries

Alexey Bokhovkin, Shubham Tulsiani, Angela Dai

Remarkable advances have been achieved recently in learning neural representations that characterize object geometry, while generating textured objects suitable for downstream appl…

cs.CV2023★ 6 cited

HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion

Ziya Erkoç, Fangchang Ma, Qi Shan +2

Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from coordinates (e.g., xyz) to signals (e.g., signed distances), have shown remarkable promise…

cs.CV2023

Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors

Ji Hou, Xiaoliang Dai, Zijian He +2

Current popular backbones in computer vision, such as Vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images. However, to more effectively understan…

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