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Novel View Synthesis

13 papers with code · Computer Vision

Synthesize a target image with an arbitrary target camera pose from given source images and their camera poses.

( Image credit: Multi-view to Novel view: Synthesizing novel views with Self-Learned Confidence )

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Greatest papers with code

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

19 Mar 2020bmild/nerf

Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x, y, z)$ and viewing direction $(\theta, \phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location.

NOVEL VIEW SYNTHESIS

Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines

2 May 2019Fyusion/LLFF

We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration.

NOVEL VIEW SYNTHESIS

Stereo Magnification: Learning View Synthesis using Multiplane Images

24 May 2018google/stereo-magnification

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.

NOVEL VIEW SYNTHESIS

HoloGAN: Unsupervised learning of 3D representations from natural images

ICCV 2019 thunguyenphuoc/HoloGAN

This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.

IMAGE GENERATION NOVEL VIEW SYNTHESIS

Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations

NeurIPS 2019 vsitzmann/scene-representation-networks

Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes.

NOVEL VIEW SYNTHESIS

Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations

NeurIPS 2019 vsitzmann/scene-representation-networks

Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes.

NOVEL VIEW SYNTHESIS

View Synthesis by Appearance Flow

11 May 2016RenYurui/Global-Flow-Local-Attention

We address the problem of novel view synthesis: given an input image, synthesizing new images of the same object or scene observed from arbitrary viewpoints.

NOVEL VIEW SYNTHESIS

Multi-view to Novel view: Synthesizing Novel Views with Self-Learned Confidence

Proceedings of the 15th European Conference on Computer Vision, 2018 shaohua0116/Multiview2Novelview

We address the task of multi-view novel view synthesis, where we are interested in synthesizing a target image with an arbitrary camera pose from given source images.

NOVEL VIEW SYNTHESIS

DeepVoxels: Learning Persistent 3D Feature Embeddings

CVPR 2019 vsitzmann/deepvoxels

In this work, we address the lack of 3D understanding of generative neural networks by introducing a persistent 3D feature embedding for view synthesis.

3D RECONSTRUCTION NOVEL VIEW SYNTHESIS

Transformable Bottleneck Networks

ICCV 2019 kyleolsz/TB-Networks

We propose a novel approach to performing fine-grained 3D manipulation of image content via a convolutional neural network, which we call the Transformable Bottleneck Network (TBN).

3D RECONSTRUCTION NOVEL VIEW SYNTHESIS