7 citations · 31 across the 13 of their papers we have counts for
15 papers
Toon3D: Seeing Cartoons from New Perspectives
Ethan Weber, Riley Peterlinz, Rohan Mathur +3
We recover the underlying 3D structure from images of cartoons and anime depicting the same scene. This is an interesting problem domain because images in creative media are often…
Learning to Taste: A Multimodal Wine Dataset
Thoranna Bender, Simon Moe Sørensen, Alireza Kashani +5
We present WineSensed, a large multimodal wine dataset for studying the relations between visual perception, language, and flavor. The dataset encompasses 897k images of wine label…
DAC: Detector-Agnostic Spatial Covariances for Deep Local Features
Javier Tirado-Garín, Frederik Warburg, Javier Civera
Current deep visual local feature detectors do not model the spatial uncertainty of detected features, producing suboptimal results in downstream applications. In this work, we pro…
Nerfbusters: Removing Ghostly Artifacts from Casually Captured NeRFs
Frederik Warburg, Ethan Weber, Matthew Tancik +2
Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory. Existing evaluation protocol…
Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification
Kilian Zepf, Selma Wanna, Marco Miani +5
Image segmentation relies heavily on neural networks which are known to be overconfident, especially when making predictions on out-of-distribution (OOD) images. This is a common s…
Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
Frederik Warburg, Marco Miani, Silas Brack +1
We propose the first Bayesian encoder for metric learning. Rather than relying on neural amortization as done in prior works, we learn a distribution over the network weights with…
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
Sara Fridovich-Keil, Giacomo Meanti, Frederik Warburg +2
We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions. Our model uses d choose 2 planes to represent a d-dimensional scene, providing a seamless way…
Searching for Structure in Unfalsifiable Claims
Peter Ebert Christensen, Frederik Warburg, Menglin Jia +1
Social media platforms give rise to an abundance of posts and comments on every topic imaginable. Many of these posts express opinions on various aspects of society, but their unfa…
SparseFormer: Attention-based Depth Completion Network
Frederik Warburg, Michael Ramamonjisoa, Manuel López-Antequera
Most pipelines for Augmented and Virtual Reality estimate the ego-motion of the camera by creating a map of sparse 3D landmarks. In this paper, we tackle the problem of depth compl…
Volumetric Disentanglement for 3D Scene Manipulation
Sagie Benaim, Frederik Warburg, Peter Ebert Christensen +1
Recently, advances in differential volumetric rendering enabled significant breakthroughs in the photo-realistic and fine-detailed reconstruction of complex 3D scenes, which is key…
Laplacian Autoencoders for Learning Stochastic Representations
Marco Miani, Frederik Warburg, Pablo Moreno-Muñoz +2
Established methods for unsupervised representation learning such as variational autoencoders produce none or poorly calibrated uncertainty estimates making it difficult to evaluat…
Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization
Andrea Vallone, Frederik Warburg, Hans Hansen +2
Place recognition and visual localization are particularly challenging in wide baseline configurations. In this paper, we contribute with the \emph{Danish Airs and Grounds} (DAG) d…
Self-Supervised Depth Completion for Active Stereo
Frederik Warburg, Daniel Hernandez-Juarez, Juan Tarrio +3
Active stereo systems are used in many robotic applications that require 3D information. These depth sensors, however, suffer from stereo artefacts and do not provide dense depth e…
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval
Frederik Warburg, Martin Jørgensen, Javier Civera +1
Uncertainty quantification in image retrieval is crucial for downstream decisions, yet it remains a challenging and largely unexplored problem. Current methods for estimating uncer…
Probabilistic Spatial Transformer Networks
Pola Schwöbel, Frederik Warburg, Martin Jørgensen +2
Spatial Transformer Networks (STNs) estimate image transformations that can improve downstream tasks by `zooming in' on relevant regions in an image. However, STNs are hard to trai…