47 citations · 87 across the 10 of their papers we have counts for
29 papers
What can we learn about a generated image corrupting its latent representation?
Agnieszka Tomczak, Aarushi Gupta, Slobodan Ilic +2
Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. They can tr…
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
Hao Yu, Ji Hou, Zheng Qin +5
Successful point cloud registration relies on accurate correspondences established upon powerful descriptors. However, existing neural descriptors either leverage a rotation-varian…
Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression
HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai +9
Depth estimation is a core task in 3D computer vision. Recent methods investigate the task of monocular depth trained with various depth sensor modalities. Every sensor has its adv…
OSOP: A Multi-Stage One Shot Object Pose Estimation Framework
Ivan Shugurov, Fu Li, Benjamin Busam +1
We present a novel one-shot method for object detection and 6 DoF pose estimation, that does not require training on target objects. At test time, it takes as input a target image…
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration
Hao Yu, Fu Li, Mahdi Saleh +2
We study the problem of extracting correspondences between a pair of point clouds for registration. For correspondence retrieval, existing works benefit from matching sparse keypoi…
DistillPose: Lightweight Camera Localization Using Auxiliary Learning
Yehya Abouelnaga, Mai Bui, Slobodan Ilic
We propose a lightweight retrieval-based pipeline to predict 6DOF camera poses from RGB images. Our pipeline uses a convolutional neural network (CNN) to encode a query image as a…