Open-Vocabulary Category-Level 9D Object Pose and Size Estimation Study

Alapfogalmak

Proposing an open-vocabulary framework for predicting object poses and sizes using text descriptions and a large-scale dataset.

Kivonat

This study introduces an open-vocabulary category-level object pose and size estimation problem. It presents a framework leveraging pre-trained models to predict normalized object coordinate space (NOCS) maps. The proposed method outperforms baselines on diverse categories, demonstrating generalizability to unseen objects. A large-scale dataset, OO3D-9D, is introduced for training. Experiments show superior performance in estimating poses and sizes of novel categories.

Statisztikák

Given human text descriptions of arbitrary novel object categories, the robot agent seeks to predict the position, orientation, and size of the target object in the observed scene image.
OO3D-9D dataset comprises 5,371 objects spanning 216 categories with annotations for symmetry axes.
The proposed method fully leverages visual semantic prior from pre-trained DinoV2 and aligned visual and language knowledge within the text-to-image diffusion model.

Idézetek

"Vision-based object pose estimation is a fundamental problem in computer vision and robotics."
"Our main contributions include introducing a new challenging problem, establishing a benchmark dataset, and proposing an open-vocabulary framework."
"The proposed method significantly outperforms baselines across all metrics."

Főbb Kivonatok

OV9D

by Junhao Cai,Y... : arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12396.pdf

Mélyebb kérdések

How can this open-vocabulary approach be applied to other vision tasks beyond object pose estimation

This open-vocabulary approach can be applied to other vision tasks beyond object pose estimation by leveraging the generalizability of pre-trained visual-language models. For instance, it could be used in tasks like object detection, semantic segmentation, depth estimation, and even more complex tasks like scene understanding or activity recognition. By utilizing the alignment learned by these models between images and text descriptions, the framework can effectively handle novel categories or instances in various vision tasks.

What are potential limitations or biases introduced by relying on pre-trained models for generalization

Relying on pre-trained models for generalization may introduce limitations or biases in several ways:

Domain Bias: Pre-trained models are often trained on specific datasets that might not fully represent the diversity of real-world scenarios. This could lead to biases towards certain types of data.
Task Specificity: The pre-training objectives of these models might not align perfectly with the requirements of a specific task, leading to suboptimal performance.
Data Distribution Mismatch: If there is a significant difference between the distribution of data used for pre-training and the target task's data distribution, it may affect generalization capabilities.
Overfitting: There is a risk of overfitting to patterns present in the pre-training data rather than learning truly generalized features.

How might advancements in this field impact real-world applications like autonomous robotics or augmented reality

Advancements in this field can have profound impacts on real-world applications like autonomous robotics and augmented reality:

Autonomous Robotics:

Improved Object Recognition: Enhanced object pose estimation can enable robots to better understand their environment and interact with objects more efficiently.
Autonomous Navigation: Accurate perception through advanced vision systems can enhance navigation capabilities for robots operating in dynamic environments.
Grasping and Manipulation: Precise object pose estimation is crucial for successful grasping and manipulation tasks performed by robotic arms.

Augmented Reality:

Enhanced Object Interaction: AR applications can benefit from accurate object recognition and localization for seamless integration of virtual elements into real-world scenes.
Real-time Visual Guidance: Advanced vision systems can provide users with real-time guidance based on environmental cues detected through AR devices.
Immersive Experiences: Improved object pose estimation allows for more realistic augmentation within AR experiences, enhancing user immersion.

Overall, advancements in open-vocabulary category-level object pose estimation have the potential to revolutionize how autonomous systems perceive their surroundings and how augmented reality enhances human interactions with digital content.