Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Warburg, Frederik"'
Autor:
Weber, Ethan, Peterlinz, Riley, Mathur, Rohan, Warburg, Frederik, Efros, Alexei A., Kanazawa, Angjoo
In this work, we recover the underlying 3D structure of non-geometrically consistent scenes. We focus our analysis on hand-drawn images from cartoons and anime. Many cartoons are created by artists without a 3D rendering engine, which means that any
Externí odkaz:
http://arxiv.org/abs/2405.10320
Autor:
Bender, Thoranna, Sørensen, Simon Moe, Kashani, Alireza, Hjorleifsson, K. Eldjarn, Hyldig, Grethe, Hauberg, Søren, Belongie, Serge, Warburg, Frederik
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 labels and 824k reviews of wines curated from the Vivino platform. It has o
Externí odkaz:
http://arxiv.org/abs/2308.16900
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 propose two post-hoc covariance estimates that can be plugged into any pr
Externí odkaz:
http://arxiv.org/abs/2305.12250
Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory. Existing evaluation protocols often do not capture these effects, since they usually only assess i
Externí odkaz:
http://arxiv.org/abs/2304.10532
Autor:
Zepf, Kilian, Wanna, Selma, Miani, Marco, Moore, Juston, Frellsen, Jes, Hauberg, Søren, Warburg, Frederik, Feragen, Aasa
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 scenario in the medical domain due to variations in equipment, acquisit
Externí odkaz:
http://arxiv.org/abs/2303.13123
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 the Laplace Approximation. We actualize this by first proving that the
Externí odkaz:
http://arxiv.org/abs/2302.01332
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 to go from static (d=3) to dynamic (d=4) scenes. This planar factoriza
Externí odkaz:
http://arxiv.org/abs/2301.10241
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 unfalsifiable nature makes them ill-suited to fact-checking pipelines. In
Externí odkaz:
http://arxiv.org/abs/2209.00495
Autor:
Miani, Marco, Warburg, Frederik, Moreno-Muñoz, Pablo, Detlefsen, Nicke Skafte, Hauberg, Søren
Established methods for unsupervised representation learning such as variational autoencoders produce none or poorly calibrated uncertainty estimates making it difficult to evaluate if learned representations are stable and reliable. In this work, we
Externí odkaz:
http://arxiv.org/abs/2206.15078
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 completion, that is, densifying this sparse 3D map using RGB images as guid
Externí odkaz:
http://arxiv.org/abs/2206.04557