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Robotic research encounters a significant hurdle when it comes to theintricate task of grasping objects that come in various shapes, materials, andtextures. Unlike many prior investigations that heavily leaned on specializedpoint-cloud cameras or abundant RGB visual data to gather 3D insights forobject-grasping missions, this paper introduces a pioneering approach calledRGBGrasp. This method depends on a limited set of RGB views to perceive the 3Dsurroundings containing transparent and specular objects and achieve accurategrasping. Our method utilizes pre-trained depth prediction models to establishgeometry constraints, enabling precise 3D structure estimation, even underlimited view conditions. Finally, we integrate hash encoding and a proposalsampler strategy to significantly accelerate the 3D reconstruction process.These innovations significantly enhance the adaptability and effectiveness ofour algorithm in real-world scenarios. Through comprehensive experimentalvalidation, we demonstrate that RGBGrasp achieves remarkable success across awide spectrum of object-grasping scenarios, establishing it as a promisingsolution for real-world robotic manipulation tasks. The demo of our method canbe found on: https://sites.google.com/view/rgbgrasp

RGBGrasp: Image-based Object Grasping by Capturing Multiple Views during Robot Arm Movement with Neural Radiance Fields