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Bio-Inspired 3D Affordance Understanding from Single Image with Neural Radiance Field for Enhanced Embodied

Zirui Guo1, Xieyuanli Chen1, Zhiqiang Zheng1

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.

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Summary

This study introduces AFF-NeRF, a novel method for generating 3D affordance models of homogeneous objects from a single image. This approach enhances robotic grasping by improving affordance understanding for unseen objects.

Keywords:
3D affordance modelsneural radiance fieldsrobotic manipulation

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Affordance understanding is critical for precise robotic manipulation, involving the identification of operable object parts.
  • Homogeneous objects, despite varied shapes, exhibit consistent affordance distributions, presenting a unique challenge and opportunity for AI.
  • Existing methods struggle with generating accurate affordance models for novel, homogeneous objects.

Purpose of the Study:

  • To develop a method for generating 3D affordance models of homogeneous objects from single images.
  • To leverage human cognitive processes in understanding object affordances for robotic applications.
  • To improve the adaptability and accuracy of robotic grasping through enhanced affordance generation.

Main Methods:

  • Proposes AFF-NeRF, a novel approach integrating deep residual networks with extended neural radiance fields.
  • Extracts shape and appearance features from objects using deep residual networks.
  • Generates 3D affordance models for unseen homogeneous objects using a single input image.

Main Results:

  • AFF-NeRF outperforms baseline methods in generating affordances for unseen views of novel objects without additional training.
  • The generated 3D affordance models lead to more stable robotic grasps when integrated into grasp generation algorithms.
  • Demonstrates robust performance across various homogeneous objects with diverse shapes.

Conclusions:

  • AFF-NeRF effectively generates accurate 3D affordance models for homogeneous objects, advancing robotic manipulation capabilities.
  • The method's ability to generalize to unseen objects from single images signifies a significant step in AI-driven grasping.
  • This research provides a foundation for more intelligent and adaptable robotic systems in complex environments.