Shape Back-Projection In 3D Scenes
This work addresses the need for efficient 3D scene analysis in domains such as robotics and autonomous vehicles, but it appears incremental as it adapts an existing color back-projection concept to 3D geometry.
The authors tackled the problem of computationally efficient point cloud processing by proposing shape back-projection, a probabilistic framework that measures similarity between 3D surfaces using shape histograms and back-projection, achieving applications like binary surface classification and real-time robotic operations.
In this work, we propose a novel framework shape back-projection for computationally efficient point cloud processing in a probabilistic manner. The primary component of the technique is shape histogram and a back-projection procedure. The technique measures similarity between 3D surfaces, by analyzing their geometrical properties. It is analogous to color back-projection which measures similarity between images, simply by looking at their color distributions. In the overall process, first, shape histogram of a sample surface (e.g. planar) is computed, which captures the profile of surface normals around a point in form of a probability distribution. Later, the histogram is back-projected onto a test surface and a likelihood score is obtained. The score depicts that how likely a point in the test surface behaves similar to the sample surface, geometrically. Shape back-projection finds its application in binary surface classification, high curvature edge detection in unorganized point cloud, automated point cloud labeling for 3D-CNNs (convolutional neural network) etc. The algorithm can also be used for real-time robotic operations such as autonomous object picking in warehouse automation, ground plane extraction for autonomous vehicles and can be deployed easily on computationally limited platforms (UAVs).