most citedDPODv2: Dense Correspondence-Based 6 DoF Pose Estimation

58 citations · 92 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2022★ 5 cited

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…

cs.CV2022★ 27 cited

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…

cs.CV2022★ 58 cited

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…

cs.CV2022★ 2 cited

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…

cs.CV2022

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…

cs.CV2019

HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects

Roman Kaskman, Sergey Zakharov, Ivan Shugurov +1

Among the most important prerequisites for creating and evaluating 6D object pose detectors are datasets with labeled 6D poses. With the advent of deep learning, demand for such da…

cs.CV2019

DPOD: 6D Pose Object Detector and Refiner

Sergey Zakharov, Ivan Shugurov, Slobodan Ilic

In this paper we present a novel deep learning method for 3D object detection and 6D pose estimation from RGB images. Our method, named DPOD (Dense Pose Object Detector), estimates…