Single-view 3D object reconstruction has seen much progress, yet methods still struggle generalizing to novel shapes unseen during training.
Active learning aims to reduce the high labeling cost involved in training machine learning models on large datasets by efficiently labeling only the most informative samples.
We present a convolutional neural network for joint 3D shape prediction and viewpoint estimation from a single input image.
The ability to understand visual information from limited labeled data is an important aspect of machine learning.
Convolutional networks for single-view object reconstruction have shown impressive performance and have become a popular subject of research.
Ranked #1 on 3D Reconstruction on 300W
Our approach is based on tangent convolutions - a new construction for convolutional networks on 3D data.
Ranked #8 on 3D Semantic Segmentation on SensatUrban
We present a deep convolutional decoder architecture that can generate volumetric 3D outputs in a compute- and memory-efficient manner by using an octree representation.
Ranked #3 on 3D Reconstruction on Data3D−R2N2
We present a convolutional network capable of inferring a 3D representation of a previously unseen object given a single image of this object.
We train generative 'up-convolutional' neural networks which are able to generate images of objects given object style, viewpoint, and color.