24 citations · 39 across the 12 of their papers we have counts for
20 papers · 1 filter
Alignist: CAD-Informed Orientation Distribution Estimation by Fusing Shape and Correspondences
Shishir Reddy Vutukur, Rasmus Laurvig Haugaard, Junwen Huang +2
Object pose distribution estimation is crucial in robotics for better path planning and handling of symmetric objects. Recent distribution estimation approaches employ contrastive…
SABER-6D: Shape Representation Based Implicit Object Pose Estimation
Shishir Reddy Vutukur, Mengkejiergeli Ba, Benjamin Busam +2
In this paper, we propose a novel encoder-decoder architecture, named SABER, to learn the 6D pose of the object in the embedding space by learning shape representation at a given p…
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection
Yongzhi Su, Yan Di, Fabian Manhardt +5
Despite monocular 3D object detection having recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery, such two-stage me…
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…