18 citations · 72 across the 34 of their papers we have counts for
6 papers · 1 filter
ILSH: The Imperial Light-Stage Head Dataset for Human Head View Synthesis
Jiali Zheng, Youngkyoon Jang, Athanasios Papaioannou +6
This paper introduces the Imperial Light-Stage Head (ILSH) dataset, a novel light-stage-captured human head dataset designed to support view synthesis academic challenges for human…
Multi-task Learning with 3D-Aware Regularization
Wei-Hong Li, Steven McDonagh, Ales Leonardis +1
Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmenta…
Image Denoising and the Generative Accumulation of Photons
Alexander Krull, Hector Basevi, Benjamin Salmon +5
We present a fresh perspective on shot noise corrupted images and noise removal. By viewing image formation as the sequential accumulation of photons on a detector grid, we show th…
Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations
Thomas Tanay, Aleš Leonardis, Matteo Maggioni
While current multi-frame restoration methods combine information from multiple input images using 2D alignment techniques, recent advances in novel view synthesis are paving the w…
HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation
Linfang Zheng, Chen Wang, Yinghan Sun +5
In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation. 3D graph convolution (3D-GC)…
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai +10
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks…