Search Results for author: Ji Hou

Found 8 papers, 6 papers with code

RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration

no code implementations27 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.

Point Cloud Registration

Panoptic 3D Scene Reconstruction From a Single RGB Image

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.

3D Instance Segmentation 3D Scene Reconstruction +4

Pri3D: Can 3D Priors Help 2D Representation Learning?

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.

Contrastive Learning Instance Segmentation +4

Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts

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.

Instance Segmentation Scene Understanding +1

RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction

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.

object-detection Object Detection +3

Deep Face Forgery Detection

1 code implementation6 Apr 2020 Nika Dogonadze, Jana Obernosterer, Ji Hou

Rapid progress in deep learning is continuously making it easier and cheaper to generate video forgeries.

Classification Face Recognition +2

RevealNet: Seeing Behind Objects in RGB-D Scans

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.

3D Reconstruction 3D Semantic Instance Segmentation +1

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