Bio-Inspired 3D Affordance Understanding from Single Image with Neural Radiance Field for Enhanced Embodied Intelligence
Year: 2025
Type: article
Abstract: Affordance understanding means identifying possible operable parts of objects, which is crucial in achieving accurate robotic manipulation. Although homogeneous objects for grasping have various shapes, they always share a similar affordance distribution. Based on this fact, we propose AFF-NeRF to address the problem of affordance generation for homogeneous objects inspired by human cognitive processes. Our method employs deep residual networks to extract the shape and appearance features of various objects, enabling it to adapt to various homogeneous objects. These features are then integrated into our extended neural radiance fields, named AFF-NeRF, to generate 3D affordance models for unseen objects using a single image. Our experimental results demonstrate that our approach outperforms baseline methods in the affordance generation of unseen views on novel objects without additional training. Additionally, more stable grasps can be obtained by employing 3D affordance models generated by our method in the grasp generation algorithm.
Source: Biomimetics
Authors: Zirui Guo, Xieyuanli Chen, Zhiqiang Zheng, Huimin Lu, Ruibin Guo
Institution: National University of Defense Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,654
OpenAlex ID: W4411457401