Search Results for author: Guocheng Qian

Found 8 papers, 7 papers with code

Pix4Point: Image Pretrained Transformers for 3D Point Cloud Understanding

1 code implementation25 Aug 2022 Guocheng Qian, Xingdi Zhang, Abdullah Hamdi, Bernard Ghanem

In the realm of 3D point clouds, the availability of large datasets is a challenge, which exacerbates the issue of training Transformers for 3D tasks.

3D Point Cloud Classification Point Cloud Classification +1

PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

1 code implementation9 Jun 2022 Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Abed Al Kader Hammoud, Mohamed Elhoseiny, Bernard Ghanem

In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions.

3D Classification 3D Part Segmentation +2

ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning

1 code implementation NeurIPS 2021 Guocheng Qian, Hasan Abed Al Kader Hammoud, Guohao Li, Ali Thabet, Bernard Ghanem

We then introduce a new Anisotropic Reduction function into our Separable SA module and propose an Anisotropic Separable SA (ASSA) module that substantially increases the network's accuracy.

3D Part Segmentation 3D Point Cloud Classification +2

DeepGCNs: Making GCNs Go as Deep as CNNs

4 code implementations15 Oct 2019 Guohao Li, Matthias Müller, Guocheng Qian, Itzel C. Delgadillo, Abdulellah Abualshour, Ali Thabet, Bernard Ghanem

This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.

3D Point Cloud Classification 3D Semantic Segmentation +1

Rethinking the Pipeline of Demosaicing, Denoising and Super-Resolution

1 code implementation7 May 2019 Guocheng Qian, Yuanhao Wang, Chao Dong, Jimmy S. Ren, Wolfgang Heidrich, Bernard Ghanem, Jinjin Gu

Such a mixture problem is usually solved by a sequential solution (applying each method independently in a fixed order: DM $\to$ DN $\to$ SR), or is simply tackled by an end-to-end network without enough analysis into interactions among tasks, resulting in an undesired performance drop in the final image quality.

Demosaicking Denoising +1

Cannot find the paper you are looking for? You can Submit a new open access paper.