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PRN: Panoptic Refinement Network
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Panoptic segmentation is the task of uniquely assigning every pixel in an image to either a semantic label or an individual object instance, generating a coherent and complete scene description. Many current panoptic segmentation methods, however, predict masks of semantic classes and object instances in separate branches, yielding inconsistent predictions. Moreover, because state-of-the-art panop...Show More
Towards Accurate Reconstruction of 3D Scene Shape From A Single Monocular Image
Wei Yin;Jianming Zhang;Oliver Wang;Simon Niklaus;Simon Chen;Yifan Liu;Chunhua Shen
Despite significant progress made in the past few years, challenges remain for depth estimation using a single monocular image. First, it is nontrivial to train a metric-depth prediction model that can generalize well to diverse scenes mainly due to limited training data. Thus, researchers have built large-scale relative depth datasets that are much easier to collect. However, existing relative de...Show More
Layered Depth Refinement with Mask Guidance
Soo Ye Kim;Jianming Zhang;Simon Niklaus;Yifei Fan;Simon Chen;Zhe Lin;Munchurl Kim
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (5)
Depth maps are used in a wide range of applications from 3D rendering to 2D image effects such as Bokeh. However, those predicted by single image depth estimation (SIDE) models often fail to capture isolated holes in objects and/or have inaccurate boundary regions. Meanwhile, high-quality masks are much easier to obtain, using commercial auto-masking tools or off-the-shelf methods of segmentation ...Show More
SSH: A Self-Supervised Framework for Image Harmonization
Yifan Jiang;He Zhang;Jianming Zhang;Yilin Wang;Zhe Lin;Kalyan Sunkavalli;Simon Chen;Sohrab Amirghodsi;Sarah Kong;Zhangyang Wang
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (43)
Image harmonization aims to improve the quality of image compositing by matching the "appearance" (e.g., color tone, brightness and contrast) between foreground and background images. However, collecting large-scale annotated datasets for this task requires complex professional retouching. Instead, we propose a novel Self-Supervised Harmonization framework (SSH) that can be trained using just "fre...Show More
Learning to Recover 3D Scene Shape from a Single Image
Wei Yin;Jianming Zhang;Oliver Wang;Simon Niklaus;Long Mai;Simon Chen;Chunhua Shen
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (123)
Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length. We investigate this problem in detail, and propose a two-stage framework that f...Show More
Modeling and removing spatially-varying optical blur
2011 IEEE International Conference on Computational Photography (ICCP)
Year: 2011 | Conference Paper |
Photo deblurring has been a major research topic in the past few years. So far, existing methods have focused on removing the blur due to camera shake and object motion. In this paper, we show that the optical system of the camera also generates significant blur, even with professional lenses. We introduce a method to estimate the blur kernel densely over the image and across multiple aperture and...Show More
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