Today I read a paper titled “Weighted Unsupervised Learning for 3D Object Detection”
The abstract is:
This paper introduces a novel weighted unsupervised learning for object detection using an RGB-D camera.
This technique is feasible for detecting the moving objects in the noisy environments that are captured by an RGB-D camera.
The main contribution of this paper is a real-time algorithm for detecting each object using weighted clustering as a separate cluster.
In a preprocessing step, the algorithm calculates the pose 3D position X, Y, Z and RGB color of each data point and then it calculates each data point’s normal vector using the point’s neighbor.
After preprocessing, our algorithm calculates k-weights for each data point; each weight indicates membership.
Resulting in clustered objects of the scene.