Search Results for author: Qingnan Fan

Found 22 papers, 8 papers with code

Multi-Robot Active Mapping via Neural Bipartite Graph Matching

no code implementations30 Mar 2022 Kai Ye, Siyan Dong, Qingnan Fan, He Wang, Li Yi, Fei Xia, Jue Wang, Baoquan Chen

Previous approaches either choose the frontier as the goal position via a myopic solution that hinders the time efficiency, or maximize the long-term value via reinforcement learning to directly regress the goal position, but does not guarantee the complete map construction.

Graph Matching reinforcement-learning

AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions

no code implementations1 Dec 2021 Yian Wang, Ruihai Wu, Kaichun Mo, Jiaqi Ke, Qingnan Fan, Leonidas Guibas, Hao Dong

Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots performing daily tasks in human environments.

ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation

no code implementations29 Jul 2021 Yanchao Yang, Hanxiang Ren, He Wang, Bokui Shen, Qingnan Fan, Youyi Zheng, C. Karen Liu, Leonidas Guibas

Furthermore, to resolve ambiguities in converting the semantic images to semantic labels, we treat the view transformation network as a functional representation of an unknown mapping implied by the color images and propose functional label hallucination to generate pseudo-labels in the target domain.

Semantic Segmentation Unsupervised Domain Adaptation

VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects

no code implementations ICLR 2022 Ruihai Wu, Yan Zhao, Kaichun Mo, Zizheng Guo, Yian Wang, Tianhao Wu, Qingnan Fan, Xuelin Chen, Leonidas Guibas, Hao Dong

In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.

Generating Manga From Illustrations via Mimicking Manga Creation Workflow

no code implementations CVPR 2021 Lvmin Zhang, Xinrui Wang, Qingnan Fan, Yi Ji, Chunping Liu

To this end, we create a large-scale dataset with these three components annotated by artists in a human-in-the-loop manner.

CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds

1 code implementation ICCV 2021 Yijia Weng, He Wang, Qiang Zhou, Yuzhe Qin, Yueqi Duan, Qingnan Fan, Baoquan Chen, Hao Su, Leonidas J. Guibas

For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories.

Frame Pose Tracking

P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding

no code implementations24 Dec 2020 Yunze Liu, Li Yi, Shanghang Zhang, Qingnan Fan, Thomas Funkhouser, Hao Dong

Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used as an initialization for more efficient and effective training on downstream tasks.

Contrastive Learning Representation Learning +1

Towards Accurate Active Camera Localization

no code implementations8 Dec 2020 Qihang Fang, Yingda Yin, Qingnan Fan, Fei Xia, Siyan Dong, Sheng Wang, Jue Wang, Leonidas Guibas, Baoquan Chen

The past solutions are mostly based on Markov Localization, which reduces the position-wise camera uncertainty for localization.

Camera Localization Pose Estimation +1

IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration

1 code implementation11 Oct 2020 Ziyi Wu, Yueqi Duan, He Wang, Qingnan Fan, Leonidas J. Guibas

The former aims to recover the surface of point cloud through implicit function, while the latter encourages evenly-distributed points.

Generative 3D Part Assembly via Dynamic Graph Learning

2 code implementations NeurIPS 2020 Jialei Huang, Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas Guibas, Hao Dong

Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation.

Graph Learning Pose Estimation +1

Deep Reflection Prior

no code implementations8 Dec 2019 Yingda Yin, Qingnan Fan, Dong-Dong Chen, Yujie Wang, Angelica Aviles-Rivero, Ruoteng Li, Carola-Bibiane Schnlieb, Dani Lischinski, Baoquan Chen

Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass.

Reflection Removal

A General Decoupled Learning Framework for Parameterized Image Operators

no code implementations11 Jul 2019 Qingnan Fan, Dong-Dong Chen, Lu Yuan, Gang Hua, Nenghai Yu, Baoquan Chen

To overcome this limitation, we propose a new decoupled learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted as the base network.

Image Smoothing via Unsupervised Learning

1 code implementation7 Nov 2018 Qingnan Fan, Jiaolong Yang, David Wipf, Baoquan Chen, Xin Tong

Image smoothing represents a fundamental component of many disparate computer vision and graphics applications.

Image Manipulation image smoothing

Decouple Learning for Parameterized Image Operators

1 code implementation ECCV 2018 Qingnan Fan, Dong-Dong Chen, Lu Yuan, Gang Hua, Nenghai Yu, Baoquan Chen

Many different deep networks have been used to approximate, accelerate or improve traditional image operators, such as image smoothing, super-resolution and denoising.

Denoising image smoothing +1

Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All? - A Tailored Approach to Single Image Reflection Removal

no code implementations29 May 2018 Daniel Heydecker, Georg Maierhofer, Angelica I. Aviles-Rivero, Qingnan Fan, Dong-Dong Chen, Carola-Bibiane Schönlieb, Sabine Süsstrunk

Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature of the problem.

Reflection Removal

A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing

1 code implementation ICCV 2017 Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David Wipf

This paper proposes a deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering.

image smoothing Reflection Removal +1

Revisiting Deep Intrinsic Image Decompositions

no code implementations CVPR 2018 Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David Wipf

While invaluable for many computer vision applications, decomposing a natural image into intrinsic reflectance and shading layers represents a challenging, underdetermined inverse problem.

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