Shape Back-Projection In 3D Scenes

TL;DR

This paper introduces a shape back-projection framework for efficient 3D point cloud analysis, utilizing shape histograms to measure surface similarity for various applications including classification and robotic tasks.

Contribution

The work presents a novel probabilistic shape back-projection method based on shape histograms, enabling efficient surface analysis and classification in 3D point clouds.

Findings

Effective in binary surface classification

Useful for high curvature edge detection

Applicable in real-time robotic operations

Abstract

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).