58 citations · 392 across the 30 of their papers we have counts for
7 papers · 1 filter
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…
Multi-View Object Pose Refinement With Differentiable Renderer
Ivan Shugurov, Ivan Pavlov, Sergey Zakharov +1
This paper introduces a novel multi-view 6 DoF object pose refinement approach focusing on improving methods trained on synthetic data. It is based on the DPOD detector, which prod…
DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation
Ivan Shugurov, Sergey Zakharov, Slobodan Ilic
We propose a three-stage 6 DoF object detection method called DPODv2 (Dense Pose Object Detector) that relies on dense correspondences. We combine a 2D object detector with a dense…
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…
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation
Fu Li, Hao Yu, Ivan Shugurov +3
Pose estimation of 3D objects in monocular images is a fundamental and long-standing problem in computer vision. Existing deep learning approaches for 6D pose estimation typically…