  Robotic research encounters a significant hurdle when it comes to the
intricate task of grasping objects that come in various shapes, materials, and
textures. Unlike many prior investigations that heavily leaned on specialized
point-cloud cameras or abundant RGB visual data to gather 3D insights for
object-grasping missions, this paper introduces a pioneering approach called
RGBGrasp. This method depends on a limited set of RGB views to perceive the 3D
surroundings containing transparent and specular objects and achieve accurate
grasping. Our method utilizes pre-trained depth prediction models to establish
geometry constraints, enabling precise 3D structure estimation, even under
limited view conditions. Finally, we integrate hash encoding and a proposal
sampler strategy to significantly accelerate the 3D reconstruction process.
These innovations significantly enhance the adaptability and effectiveness of
our algorithm in real-world scenarios. Through comprehensive experimental
validations, we demonstrate that RGBGrasp achieves remarkable success across a
wide spectrum of object-grasping scenarios, establishing it as a promising
solution for real-world robotic manipulation tasks. The demonstrations of our
method can be found on: https://sites.google.com/view/rgbgrasp
