MV-ROPE: Multi-view Constraints for Robust Category-level Object Pose and Size Estimation

Centrala begrepp

Utilizing multi-view RGB video streams, MV-ROPE provides a novel framework for robust and accurate category-level object pose estimation.

Sammanfattning

The content introduces MV-ROPE, a framework for category-level object pose and size estimation using RGB video streams. It consists of a scale-aware monocular dense SLAM solution, an object pose predictor, and an object-level pose graph optimizer. The method aims to overcome limitations of single-view methods by leveraging temporal information from video streams. Experimental results demonstrate the effectiveness of MV-ROPE in scenarios with limited depth input quality.
I. Introduction

Importance of object pose and size estimation.
Distinction between instance-level and category-level pose estimation.
II. Related Works

Comparison with object pose tracking methods.
Overview of different approaches to category-level pose estimation.
III. Methodology
A. Overview

Description of the framework components.
B. Camera Pose and Metric Depth Estimation

Reprojection error minimization objective.
Scale constraints in bundle adjustment layer.
C. Single View Object Pose Estimation

Instance segmentation and object association.
Object pose predictor using NOCS maps.
D. Object-Level Pose Graph Optimization

Assumptions about static objects for optimization.
IV. Experiments
A. Implementation Details

Details on optical flow estimator, dense bundle adjustment layer, and object pose predictor training strategy.
B. MEREAL Dataset

Description of dataset containing sequences from different depth sensors.
C. Results on NOCS Dataset

Performance comparison with baseline methods on the REAL test split.
D. Results on MEREAL Dataset

Comparison criteria used for evaluation on the MEREAL dataset.
V. Conclusions
The proposed MV-ROPE framework offers a promising solution for category-level object pose estimation by leveraging multi-view RGB video streams.

Statistik

MV-ROPE exhibits comparable performance to state-of-the-art RGB-D methods when utilizing high-quality depth information datasets.

Citat

"Utilizing public dataset sequences with high-quality depth information, MV-ROPE shows comparable performance to existing RGB-D methods."
"Our experimental results demonstrate significant advantages in scenarios where depth input is absent or of low quality."

Viktiga insikter från

MV-ROPE

by Jiaqi Yang,Y... på arxiv.org 03-25-2024

https://arxiv.org/pdf/2308.08856.pdf

Djupare frågor

How can MV-ROPE's reliance on multi-view RGB video streams impact its real-world applicability compared to traditional single-view methods

MV-ROPE's reliance on multi-view RGB video streams can significantly enhance its real-world applicability compared to traditional single-view methods. By utilizing multiple views, MV-ROPE can capture a more comprehensive understanding of the scene, leading to improved object pose and size estimation accuracy. This approach allows for better handling of occlusions, ambiguities, and complex object geometries that may not be fully captured in a single view. Additionally, the temporal information from continuous video streams enables MV-ROPE to track objects more robustly over time, providing a more holistic perception of the environment.

What are the potential limitations or challenges faced by MV-Rope in scenarios with dynamic environments or fast-moving objects

In scenarios with dynamic environments or fast-moving objects, MV-ROPE may face certain limitations or challenges. The framework relies on accurate camera poses and depth estimations obtained from scale-aware dense monocular SLAM modules. Fast movements or sudden changes in the scene can introduce motion blur or tracking errors, impacting the quality of input data for object pose estimation. Moreover, rapid changes in lighting conditions or scene dynamics may affect feature extraction and matching across frames, potentially leading to inaccuracies in object localization and size estimation.

How might advancements in sensor technology influence the scalability and adoption of frameworks like MV-Rope in various industries beyond robotics

Advancements in sensor technology play a crucial role in influencing the scalability and adoption of frameworks like MV-ROPE across various industries beyond robotics. Improved sensors with higher resolution, enhanced depth sensing capabilities, and reduced noise levels can enhance the quality of input data for MV-ROPE's algorithms. This leads to more accurate object pose and size estimations even in challenging real-world scenarios. Furthermore, advancements such as miniaturization of sensors, increased energy efficiency, and cost-effectiveness contribute to making frameworks like MV-ROPE more accessible for widespread deployment across industries such as augmented reality applications, autonomous vehicles navigation systems,and industrial automation processes.