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6-DoF robotic grasping is a long-lasting but unsolved problem. Recent methodsutilize strong 3D networks to extract geometric grasping representations fromdepth sensors, demonstrating superior accuracy on common objects but performunsatisfactorily on photometrically challenging objects, e.g., objects intransparent or reflective materials. The bottleneck lies in that the surface ofthese objects can not reflect back accurate depth due to the absorption orrefraction of light. In this paper, in contrast to exploiting the inaccuratedepth data, we propose the first RGB-only 6-DoF grasping pipeline calledMonoGraspNet that utilizes stable 2D features to simultaneously handlearbitrary object grasping and overcome the problems induced by photometricallychallenging objects. MonoGraspNet leverages keypoint heatmap and normal map torecover the 6-DoF grasping poses represented by our novel representationparameterized with 2D keypoints with corresponding depth, grasping direction,grasping width, and angle. Extensive experiments in real scenes demonstratethat our method can achieve competitive results in grasping common objects andsurpass the depth-based competitor by a large margin in graspingphotometrically challenging objects. To further stimulate robotic manipulationresearch, we additionally annotate and open-source a multi-view and multi-scenereal-world grasping dataset, containing 120 objects of mixed photometriccomplexity with 20M accurate grasping labels.

MonoGraspNet: 6-DoF Grasping with a Single RGB Image