Real-time Background-aware 3D Textureless Object Pose Estimation

07/22/2019
by   Mang Shao, et al.
5

In this work, we present a modified fuzzy decision forest for real-time 3D object pose estimation based on typical template representation. We employ an extra preemptive background rejector node in the decision forest framework to terminate the examination of background locations as early as possible, result in a significantly improvement on efficiency. Our approach is also scalable to large dataset since the tree structure naturally provides a logarithm time complexity to the number of objects. Finally we further reduce the validation stage with a fast breadth-first scheme. The results show that our approach outperform the state-of-the-arts on the efficiency while maintaining a comparable accuracy.

READ FULL TEXT

page 4

page 6

research
11/21/2018

Real-Time 6D Object Pose Estimation on CPU

We propose a fast and accurate 6D object pose estimation from a RGB-D im...
research
12/06/2018

Segmentation-driven 6D Object Pose Estimation

The most recent trend in estimating the 6D pose of rigid objects has bee...
research
04/20/2022

HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation

Real-time 6D object pose estimation is essential for many real-world app...
research
05/22/2023

You Only Look at One: Category-Level Object Representations for Pose Estimation From a Single Example

In order to meaningfully interact with the world, robot manipulators mus...
research
10/11/2022

CASAPose: Class-Adaptive and Semantic-Aware Multi-Object Pose Estimation

Applications in the field of augmented reality or robotics often require...
research
02/03/2016

Latent-Class Hough Forests for 6 DoF Object Pose Estimation

In this paper we present Latent-Class Hough Forests, a method for object...
research
03/12/2023

Module-Wise Network Quantization for 6D Object Pose Estimation

Many edge applications, such as collaborative robotics and spacecraft re...

Please sign up or login with your details

Forgot password? Click here to reset