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.
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.
Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.
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.
Reconstructing 3D shapes from single-view images has been a long-standing research problem.
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.
Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision.
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.
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.
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.
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.
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.
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.
Ranked #7 on Aesthetics Quality Assessment on AVA
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.
Our system leverages a Convolutional-Neural-Network model to generate location proposals of salient objects.
We propose a deep multi-patch aggregation network training approach, which allows us to train models using multiple patches generated from one image.
Ranked #8 on Aesthetics Quality Assessment on AVA
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)