| Name: | Ji Hou (侯骥） |
|---|---|
| Position: | Ph.D Candidate |
| E-Mail: | ji.hou@tum.de |
| Phone: | +49-89-289-19595 |
| Room No: | 02.13.35 |
Bio
I'm a first-year Ph.D Candidate at the Visual Computing Lab headed by Prof. Matthias Nießner. I received my Master's degree in Computer Science in RWTH Aachen University. Currently, my research focus on 3D Scene Understanding.Research Interest
- Computer Vision and Deep Learning
- 3D Scene Understanding
- Conditional Generative Models
Publications
2021
| RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction |
|---|
| Yinyu Nie, Ji Hou, Xiaoguang Han, Matthias Nießner |
| CVPR 2021 |
| We introduce RfD-Net that jointly detects and reconstructs dense object surfaces from raw point clouds. It leverages the sparsity of point cloud data and focuses on predicting shapes that are recognized with high objectness. It not only eases the difficulty of learning 2-D manifold surfaces from sparse 3D space, the point clouds in each object proposal convey shape details that support implicit function learning to reconstruct any high-resolution surfaces. |
| [video][code][bibtex][project page] |
| Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts |
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| Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie |
| CVPR 2021 (Oral) |
| Our study reveals that exhaustive labelling of 3D point clouds might be unnecessary; and remarkably, on ScanNet, even using 0.1% of point labels, we still achieve 89% (instance segmentation) and 96% (semantic segmentation) of the baseline performance that uses full annotations. |
| [video][bibtex][project page] |
2020
| RevealNet: Seeing Behind Objects in RGB-D Scans |
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| Ji Hou, Angela Dai, Matthias Nießner |
| CVPR 2020 |
| This paper introduces the task of semantic instance completion: from an incomplete, RGB-D scan of a scene, we detect the individual object instances comprising the scene and jointly infer their complete object geometry. |
| [bibtex][project page] |
2019
| 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans |
|---|
| Ji Hou, Angela Dai, Matthias Nießner |
| CVPR 2019 (Oral) |
| We introduce 3D-SIS, a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans. |
| [video][code][bibtex][project page] |