Events Calendar

PhD Defense - Guan Pang

Title: 3D Object Detection in Industrial Site Point Clouds

Location: SAL 322

Time: 12:00pm - 2:00pm, May 5th, 2016

PhD Candidate: Guan Pang

Committee members:

Prof. Ulrich Neumann (Chair)
Prof. Aiichiro Nakano
Prof. C.-C. Jay Kuo (Outside Member)

Abstract:

Detection of three dimensional (3D) objects in point clouds is a challenging problem. Existing methods either focus on a specific type of object or scene, or require prior segmentation, both of which are usually inapplicable on real-world industrial applications.

This thesis describe three methods to tackle the problem, with gradually improving performance and efficiency. The first is a general purpose 3D object detection method that combines Adaboost with 3D local features, without requirement for prior object segmentation. Experiments demonstrated competitive accuracy and robustness to occlusion, but this method suffers from limited rotation invariance. As an improvement, another method is presented with a multi-view detection approach that projects the 3D point clouds into several 2D depth images from multiple viewpoints, transforming the 3D problem into a series of 2D problems, which reduces complexity, stabilizes performance, and achieves rotation invariance. The problem is the huge amount of projected views and rotations that need to be individually detected, limiting the complexity and performance of 2D algorithm choice. Thus the third method is proposed to solve this with the introduction of convolutional neural network, because it can handle all viewpoints and rotations for the same class of object together, as well as predicting multiple classes of objects with the same network, without the need for individual detector for each object class. The detection efficiency is further improved by concatenating two extra levels of early rejection networks with binary outputs before the multi-class detection network.

3D object detection in point clouds is crucial for 3D industrial point cloud modeling. Prior efforts focus on primitive geometry, street structures or indoor objects, but industrial data has rarely been pursued. We integrate several algorithm components into an automatic 3D modeling system for industrial site point clouds, including modules for pipe modeling, plane classification and object detection, and solves the technology gaps revealed during the integration. The integrated system is able to produce classified models of large and complex industrial scenes with a quality that outperforms leading commercial software and comparable to professional hand-made models.

This thesis also describes an earlier work in multi-modal image matching which inspires later research in 3D object detection by 2D projections. Most existing 2D descriptors only work well on images of a single modality with similar texture. This proposal presents a novel basic descriptor unit called a Gixel, which uses an additive scoring method to sample surrounding edge information. Several Gixels in a circular array create the Gixel Array Descriptor, excelling in multi-modal image matching with dominant line features.

Location: Henry Salvatori Computer Science Center (SAL) - 322

Audiences: Everyone Is Invited

Contact: Lizsl De Leon