6 citations · 15 across the 13 of their papers we have counts for
4 papers · 1 filter
S2P3: Self-Supervised Polarimetric Pose Prediction
Patrick Ruhkamp, Daoyi Gao, Nassir Navab +1
This paper proposes the first self-supervised 6D object pose prediction from multimodal RGB+polarimetric images. The novel training paradigm comprises 1) a physical model to extrac…
Polarimetric Information for Multi-Modal 6D Pose Estimation of Photometrically Challenging Objects with Limited Data
Patrick Ruhkamp, Daoyi Gao, HyunJun Jung +2
6D pose estimation pipelines that rely on RGB-only or RGB-D data show limitations for photometrically challenging objects with e.g. textureless surfaces, reflections or transparenc…
Multi-Modal Dataset Acquisition for Photometrically Challenging Object
HyunJun Jung, Patrick Ruhkamp, Nassir Navab +1
This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging ob…
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…