no code implementations • 27 Sep 2022 • Hao Yu, Ji Hou, Zheng Qin, Mahdi Saleh, Ivan Shugurov, Kai Wang, Benjamin Busam, Slobodan Ilic
More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions.
1 code implementation • NeurIPS 2021 • Manuel Dahnert, Ji Hou, Matthias Nießner, Angela Dai
Inspired by 2D panoptic segmentation, we propose to unify the tasks of geometric reconstruction, 3D semantic segmentation, and 3D instance segmentation into the task of panoptic 3D scene reconstruction - from a single RGB image, predicting the complete geometric reconstruction of the scene in the camera frustum of the image, along with semantic and instance segmentations.
1 code implementation • ICCV 2021 • Ji Hou, Saining Xie, Benjamin Graham, Angela Dai, Matthias Nießner
Inspired by these advances in geometric understanding, we aim to imbue image-based perception with representations learned under geometric constraints.
1 code implementation • CVPR 2021 • Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie
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.
1 code implementation • CVPR 2021 • Yinyu Nie, Ji Hou, Xiaoguang Han, Matthias Nießner
In this work, we introduce RfD-Net that jointly detects and reconstructs dense object surfaces directly from raw point clouds.
1 code implementation • 6 Apr 2020 • Nika Dogonadze, Jana Obernosterer, Ji Hou
Rapid progress in deep learning is continuously making it easier and cheaper to generate video forgeries.
no code implementations • CVPR 2020 • Ji Hou, Angela Dai, Matthias Nießner
Thus, we introduce the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances and infer their complete object geometry.
1 code implementation • CVPR 2019 • 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.
Ranked #3 on
3D Semantic Instance Segmentation
on ScanNetV2