Search Results for author: Michael Niemeyer

Found 10 papers, 6 papers with code

Shape As Points: A Differentiable Poisson Solver

no code implementations7 Jun 2021 Songyou Peng, Chiyu "Max" Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, Andreas Geiger

However, the implicit nature of neural implicit representations results in slow inference time and requires careful initialization.

3D Reconstruction

CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields

no code implementations31 Mar 2021 Michael Niemeyer, Andreas Geiger

At test time, our model generates images with explicit control over the camera as well as the shape and appearance of the scene.

Image Generation

GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields

1 code implementation CVPR 2021 Michael Niemeyer, Andreas Geiger

While several recent works investigate how to disentangle underlying factors of variation in the data, most of them operate in 2D and hence ignore that our world is three-dimensional.

Image Generation Neural Rendering

GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis

1 code implementation NeurIPS 2020 Katja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas Geiger

In contrast to voxel-based representations, radiance fields are not confined to a coarse discretization of the 3D space, yet allow for disentangling camera and scene properties while degrading gracefully in the presence of reconstruction ambiguity.

Image Generation Novel View Synthesis +1

Learning Implicit Surface Light Fields

2 code implementations27 Mar 2020 Michael Oechsle, Michael Niemeyer, Lars Mescheder, Thilo Strauss, Andreas Geiger

In this work, we propose a novel implicit representation for capturing the visual appearance of an object in terms of its surface light field.

3D Reconstruction Image Generation

Convolutional Occupancy Networks

2 code implementations ECCV 2020 Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, Andreas Geiger

Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction.

3D Reconstruction

Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics

no code implementations ICCV 2019 Michael Niemeyer, Lars Mescheder, Michael Oechsle, Andreas Geiger

In order to perform dense 4D reconstruction from images or sparse point clouds, we combine our method with a continuous 3D representation.

3D Reconstruction

Texture Fields: Learning Texture Representations in Function Space

no code implementations ICCV 2019 Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, Andreas Geiger

A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques.

Cannot find the paper you are looking for? You can Submit a new open access paper.