Weighted Unsupervised Learning for 3D Object Detection
This addresses moving object detection in noisy settings for robotics or autonomous systems, but appears incremental as it builds on existing clustering techniques.
The paper tackles 3D object detection in noisy RGB-D camera environments by proposing a weighted unsupervised learning method that uses weighted clustering to separate objects into distinct clusters, achieving real-time performance.
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.