V. Sitzmann

Publications33
h-index17
Citations2,557
Highly Influential Citations253
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Implicit Neural Representations with Periodic Activation Functions
TLDR
This work proposes to leverage periodic activation functions for implicit neural representations and demonstrates that these networks, dubbed sinusoidal representation networks or Sirens, are ideally suited for representing complex natural signals and their derivatives.
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
TLDR
The proposed Scene Representation Networks (SRNs), a continuous, 3D-structure-aware scene representation that encodes both geometry and appearance, are demonstrated by evaluating them for novel view synthesis, few-shot reconstruction, joint shape and appearance interpolation, and unsupervised discovery of a non-rigid face model.
DeepVoxels: Learning Persistent 3D Feature Embeddings
TLDR
This work proposes DeepVoxels, a learned representation that encodes the view-dependent appearance of a 3D scene without having to explicitly model its geometry, based on a Cartesian 3D grid of persistent embedded features that learn to make use of the underlying3D scene structure.
Saliency in VR: How Do People Explore Virtual Environments?
TLDR
This work captures and analyzes gaze and head orientation data of 169 users exploring stereoscopic, static omni-directional panoramas, for a total of 1980 head and gaze trajectories for three different viewing conditions, which leads to several important insights, such as the existence of a particular fixation bias.
Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification
TLDR
This work proposes a design for an optical convolutional layer based on an optimized diffractive optical element and demonstrates in simulation and with an optical prototype that the classification accuracies of the optical systems rival those of the analogous electronic implementations, while providing substantial savings on computational cost.
End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging
TLDR
A fully-differentiable simulation model is built that maps the true source image to the reconstructed one and jointly optimize the optical parameters and the image processing algorithm parameters so as to minimize the deviation between the true and reconstructed image, over a large set of images.
Unrolled Optimization with Deep Priors
TLDR
This paper presents unrolled optimization with deep priors, a principled framework for infusing knowledge of the image formation into deep networks that solve inverse problems in imaging, inspired by classical iterative methods.
MetaSDF: Meta-learning Signed Distance Functions
TLDR
This work formalizes learning of a shape space as a meta-learning problem and leverage gradient-based meta- learning algorithms to solve this task and demonstrates that the proposed gradient- based method outperforms encoder-decoder based methods that leverage pooling-based set encoders.
Towards a Machine-Learning Approach for Sickness Prediction in 360° Stereoscopic Videos
TLDR
This work builds a dataset of stereoscopic 3D videos and their corresponding sickness ratings in order to quantify their nauseogenicity, and trains a machine learning algorithm on hand-crafted features from each video, learning the contributions of these various features to the sickness ratings.
State of the Art on Neural Rendering
TLDR
This state‐of‐the‐art report summarizes the recent trends and applications of neural rendering and focuses on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photorealistic outputs.
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