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Found 8 papers, 3 papers with code
Quark: Real-time, High-resolution, and General Neural View Synthesis
Our feed-forward network generalizes across a wide variety of datasets and scenes and produces state-of-the-art quality for a real-time method.
DeepView: View Synthesis with Learned Gradient Descent
We present a novel approach to view synthesis using multiplane images (MPIs).
DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality
We present a learning-based method to infer plausible high dynamic range (HDR), omnidirectional illumination given an unconstrained, low dynamic range (LDR) image from a mobile phone camera with a limited field of view (FOV).
Stereo Magnification: Learning View Synthesis using Multiplane Images
The view synthesis problem--generating novel views of a scene from known imagery--has garnered recent attention due in part to compelling applications in virtual and augmented reality.
3D Bounding Box Estimation Using Deep Learning and Geometry
In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D bounding box.
Ranked #9 on
Vehicle Pose Estimation
on KITTI Cars Hard
Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons
For this, we consider the neurons in the hidden layer as neural words, and construct a set of geometric neural phrases on top of them.
DeepStereo: Learning to Predict New Views from the World's Imagery
To our knowledge, our work is the first to apply deep learning to the problem of new view synthesis from sets of real-world, natural imagery.
Representing Data by a Mixture of Activated Simplices
We show that under this restriction, building a model with simplices amounts to constructing a convex hull inside the sphere whose boundary facets is close to the data.
