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Found 14 papers, 6 papers with code
Learning Neural Light Fields With Ray-Space Embedding
Our method supports rendering with a single network evaluation per pixel for small baseline light fields and with only a few evaluations per pixel for light fields with larger baselines.
Boosting View Synthesis With Residual Transfer
We present a simple but effective technique to boost the rendering quality, which can be easily integrated with most view synthesis methods.
Learning Neural Light Fields with Ray-Space Embedding Networks
Our method supports rendering with a single network evaluation per pixel for small baseline light field datasets and can also be applied to larger baselines with only a few evaluations per pixel.
Dynamic View Synthesis from Dynamic Monocular Video
We present an algorithm for generating novel views at arbitrary viewpoints and any input time step given a monocular video of a dynamic scene.
Robust Consistent Video Depth Estimation
We present an algorithm for estimating consistent dense depth maps and camera poses from a monocular video.
Space-time Neural Irradiance Fields for Free-Viewpoint Video
We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes from a single video.
Flow-edge Guided Video Completion
We present a new flow-based video completion algorithm.
Ranked #3 on
Video Inpainting
on DAVIS
One Shot 3D Photography
3D photos are static in time, like traditional photos, but are displayed with interactive parallax on mobile or desktop screens, as well as on Virtual Reality devices, where viewing it also includes stereo.
Consistent Video Depth Estimation
We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video.
3D Photography using Context-aware Layered Depth Inpainting
We propose a method for converting a single RGB-D input image into a 3D photo - a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view.
DeepMVS: Learning Multi-view Stereopsis
We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction.
Co-segmentation for Space-Time Co-located Collections
Thus, to disambiguate what the common foreground object is, we introduce a weakly-supervised technique, where we assume only a small seed, given in the form of a single segmented image.
Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction
Furthermore, using only geometric accuracy by itself does not allow evaluating systems that either lack a geometric scene representation or utilize coarse proxy geometry.
Unsupervised Joint Object Discovery and Segmentation in Internet Images
In contrast to previous co-segmentation methods, our algorithm performs well even in the presence of significant amounts of noise images (images not containing a common object), as typical for datasets collected from Internet search.
