15 citations · 18 across the 6 of their papers we have counts for
7 papers
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition
Shiming Ge, Kangkai Zhang, Haolin Liu +4
In spite of great success in many image recognition tasks achieved by recent deep models, directly applying them to recognize low-resolution images may suffer from low accuracy due…
LASA: Instance Reconstruction from Real Scans using A Large-scale Aligned Shape Annotation Dataset
Haolin Liu, Chongjie Ye, Yinyu Nie +2
Instance shape reconstruction from a 3D scene involves recovering the full geometries of multiple objects at the semantic instance level. Many methods leverage data-driven learning…
MVImgNet: A Large-scale Dataset of Multi-view Images
Xianggang Yu, Mutian Xu, Yidan Zhang +10
Being data-driven is one of the most iconic properties of deep learning algorithms. The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer…
Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes
Haolin Liu, Yujian Zheng, Guanying Chen +2
We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images. Existing methods can only produce 3D…
NeRF-In: Free-Form NeRF Inpainting with RGB-D Priors
Hao-Kang Liu, I-Chao Shen, Bing-Yu Chen
Though Neural Radiance Field (NeRF) demonstrates compelling novel view synthesis results, it is still unintuitive to edit a pre-trained NeRF because the neural network's parameters…
TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes
Mutian Xu, Pei Chen, Haolin Liu +1
Many basic indoor activities such as eating or writing are always conducted upon different tabletops (e.g., coffee tables, writing desks). It is indispensable to understanding tabl…
Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images
Haolin Liu, Anran Lin, Xiaoguang Han +3
Grounding referring expressions in RGBD image has been an emerging field. We present a novel task of 3D visual grounding in single-view RGBD image where the referred objects are of…