8 citations · 16 across the 2 of their papers we have counts for
5 papers
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts
Ji Hou, Benjamin Graham, Matthias Nießner +1
The rapid progress in 3D scene understanding has come with growing demand for data; however, collecting and annotating 3D scenes (e.g. point clouds) are notoriously hard. For examp…
RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction
Yinyu Nie, Ji Hou, Xiaoguang Han +1
Semantic scene understanding from point clouds is particularly challenging as the points reflect only a sparse set of the underlying 3D geometry. Previous works often convert point…
Deep Face Forgery Detection
Nika Dogonadze, Jana Obernosterer, Ji Hou
Rapid progress in deep learning is continuously making it easier and cheaper to generate video forgeries. Hence, it becomes very important to have a reliable way of detecting these…
RevealNet: Seeing Behind Objects in RGB-D Scans
Ji Hou, Angela Dai, Matthias Nießner
During 3D reconstruction, it is often the case that people cannot scan each individual object from all views, resulting in missing geometry in the captured scan. This missing geome…
3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans
Ji Hou, Angela Dai, Matthias Nießner
We introduce 3D-SIS, a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans. The core idea of our method is to jointly learn from both g…