Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a textureless object segmentation approach for autonomous service robots acting in human living environments. The proposed system allows a robot to effectively segment textureless objects in cluttered scenes by leveraging its manipulation capabilities. In our pipeline, the cluttered scenes are first statically segmented using state-of-the-art classification algorithm and then the interactive segmentation is deployed in order to resolve this possibly ambiguous static segmentation. In the second step the RGBD (RGB + Depth) sparse features, estimated on the RGBD point cloud from the Kinect sensor, are extracted and tracked while motion is induced into a scene. Using the resulting feature poses, the features are then assigned to their corresponding objects by means of a graph-based clustering algorithm. In the final step,  reconstruct the dense models of the objects from the previously clustered sparse RGBD features.  evaluated the approach on a set of scenes which consist of various textureless flat (e.g. box-like) and round (e.g. cylinder-like) objects and the combinations thereof.
['Karol Hausman', 'Ferenc Balint-Benczedi', 'Dejan Pangercic', 'Zoltan Csaba Marton', 'Ryohei Ueda', 'Kei Okada', 'Michael Beetz']
I am writing a research paper on a idea Tracking-based interactive segmentation of textureless objects
[{"from": "human", "value": "I am writing a research paper on a idea Tracking-based interactive segmentation of textureless objects"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a textureless object segmentation approach for autonomous service robots acting in human living environments. The proposed system allows a robot to effectively segment textureless objects in cluttered scenes by leveraging its manipulation capabilities. In our pipeline, the cluttered scenes are first statically segmented using state-of-the-art classification algorithm and then the interactive segmentation is deployed in order to resolve this possibly ambiguous static segmentation. In the second step the RGBD (RGB + Depth) sparse features, estimated on the RGBD point cloud from the Kinect sensor, are extracted and tracked while motion is induced into a scene. Using the resulting feature poses, the features are then assigned to their corresponding objects by means of a graph-based clustering algorithm. In the final step,  reconstruct the dense models of the objects from the previously clustered sparse RGBD features.  evaluated the approach on a set of scenes which consist of various textureless flat (e.g. box-like) and round (e.g. cylinder-like) objects and the combinations thereof."}]