Search Results for author: Radomir Mech

Found 18 papers, 9 papers with code

CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds

1 code implementation ICCV 2021 Eric-Tuan Lê, Minhyuk Sung, Duygu Ceylan, Radomir Mech, Tamy Boubekeur, Niloy J. Mitra

We present Cascaded Primitive Fitting Networks (CPFN) that relies on an adaptive patch sampling network to assemble detection results of global and local primitive detection networks.

DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates

1 code implementation CVPR 2021 Minghua Liu, Minhyuk Sung, Radomir Mech, Hao Su

Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles.

FAME: 3D Shape Generation via Functionality-Aware Model Evolution

1 code implementation9 May 2020 Yanran Guan, Han Liu, Kun Liu, Kangxue Yin, Ruizhen Hu, Oliver van Kaick, Yan Zhang, Ersin Yumer, Nathan Carr, Radomir Mech, Hao Zhang

Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.

Graphics

Learning Generative Models of Shape Handles

no code implementations CVPR 2020 Matheus Gadelha, Giorgio Gori, Duygu Ceylan, Radomir Mech, Nathan Carr, Tamy Boubekeur, Rui Wang, Subhransu Maji

We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations.

3DN: 3D Deformation Network

1 code implementation CVPR 2019 Weiyue Wang, Duygu Ceylan, Radomir Mech, Ulrich Neumann

Given such a source 3D model and a target which can be a 2D image, 3D model, or a point cloud acquired as a depth scan, we introduce 3DN, an end-to-end network that deforms the source model to resemble the target.

3D Shape Generation

Photo-Sketching: Inferring Contour Drawings from Images

2 code implementations2 Jan 2019 Mengtian Li, Zhe Lin, Radomir Mech, Ersin Yumer, Deva Ramanan

Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision.

Boundary Detection BSDS500

Sequence-to-Segment Networks for Segment Detection

no code implementations NeurIPS 2018 Zijun Wei, Boyu Wang, Minh Hoai Nguyen, Jianming Zhang, Zhe Lin, Xiaohui Shen, Radomir Mech, Dimitris Samaras

Detecting segments of interest from an input sequence is a challenging problem which often requires not only good knowledge of individual target segments, but also contextual understanding of the entire input sequence and the relationships between the target segments.

Temporal Action Proposal Generation Video Summarization

Personalized Image Aesthetics

no code implementations ICCV 2017 Jian Ren, Xiaohui Shen, Zhe Lin, Radomir Mech, David J. Foran

To accommodate our study, we first collect two distinct datasets, a large image dataset from Flickr and annotated by Amazon Mechanical Turk, and a small dataset of real personal albums rated by owners.

Active Learning

Recognizing and Curating Photo Albums via Event-Specific Image Importance

no code implementations19 Jul 2017 Yufei Wang, Zhe Lin, Xiaohui Shen, Radomir Mech, Gavin Miller, Garrison W. Cottrell

Automatic organization of personal photos is a problem with many real world ap- plications, and can be divided into two main tasks: recognizing the event type of the photo collection, and selecting interesting images from the collection.

Learning to Detect Multiple Photographic Defects

1 code implementation6 Dec 2016 Ning Yu, Xiaohui Shen, Zhe Lin, Radomir Mech, Connelly Barnes

Our new dataset enables us to formulate the problem as a multi-task learning problem and train a multi-column deep convolutional neural network (CNN) to simultaneously predict the severity of all the defects.

Defect Detection Multi-Task Learning

Salient Object Subitizing

no code implementations CVPR 2015 Jianming Zhang, Shugao Ma, Mehrnoosh Sameki, Stan Sclaroff, Margrit Betke, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech

We study the problem of Salient Object Subitizing, i. e. predicting the existence and the number of salient objects in an image using holistic cues.

Image Retrieval RGB Salient Object Detection +1

Photo Aesthetics Ranking Network with Attributes and Content Adaptation

2 code implementations6 Jun 2016 Shu Kong, Xiaohui Shen, Zhe Lin, Radomir Mech, Charless Fowlkes

In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function.

Aesthetics Quality Assessment

Event-Specific Image Importance

no code implementations CVPR 2016 Yufei Wang, Zhe Lin, Xiaohui Shen, Radomir Mech, Gavin Miller, Garrison W. Cottrell

In this paper, we show that the selection of important images is consistent among different viewers, and that this selection process is related to the event type of the album.

Minimum Barrier Salient Object Detection at 80 FPS

no code implementations ICCV 2015 Jianming Zhang, Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech

Powered by this fast MBD transform algorithm, the proposed salient object detection method runs at 80 FPS, and significantly outperforms previous methods with similar speed on four large benchmark datasets, and achieves comparable or better performance than state-of-the-art methods.

Ranked #6 on Video Salient Object Detection on DAVSOD-easy35 (using extra training data)

Salient Object Detection Video Salient Object Detection

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