Events Calendar

PhD Defense - Zhuoliang Kang

Ph.D candidate: Zhuoliang Kang

Title: Accurate 3D Model Acquisition from Imagery Data

Date: Friday, April 3, 10:00 AM

Location: EEB 131A

Committee:
Prof. Gerard Medioni (chair)
Prof. Hao Li
Prof. Alexander Sawchuk (outside member)

Abstract:

Acquisition of 3D models from 2D imagery has been essential for various applications. In particular, this dissertation investigates two important application scenarios: city-scale 3D reconstruction from aerial imagery and general 3D model acquisition with a commodity camera.

The first part of this dissertation explores an online solution to the problem. We propose an approach to solve camera pose estimation and dense reconstruction from Wide Area Aerial Surveillance (WAAS) videos captured by an airborne platform. Our approach solves them in an online fashion: it incrementally updates a sparse 3D map and estimates the camera pose as each new frame arrives; depth maps of selected key frames are computed using a variational method and integrated to produce a full 3D model via volumetric reconstruction. In practice, WAAS videos are usually captured using a multi-camera system. We parallelize our approach on multiple GPUs to efficiently handle the multi-camera imagery. The approach is also extended for progressive 3D scanning with a hand-held camera.

In many scenarios, online approach is not a necessity and accuracy has higher priority over efficiency. In the second part, we present two offline solutions. The first work generates dense 3D model based on depth map fusion, which combines variational multi-scale depth map estimation with volumetric reconstruction. We also present MeshRecon, a mesh-based offline system composed of three modules: a dense point cloud is generated using multi-resolution plane sweep method; an initial mesh model is extracted from the point cloud via global optimization considering visibility information of all images; the mesh model is then iteratively refined to capture structural details by optimizing the photometric consistency and spatial regularization. The major processes are also parallelized on GPU for efficiency. We validate its performance on real-world objects of different types at different scales in both indoor and outdoor environments. For aerial imagery case, we evaluate the approach on several real-world aerial imagery datasets each covering an urban scenario of several square kilometers. Quantitative result shows that the reconstructed model is highly accurate with mean error smaller than 1 meter over the entire city. Based on city 3D models generated at different times, we present a system for city-scale geometric change detection by performing comparisons at the 3D geometry level. Our system is able to detect geometric changes at different scales, ranging from a building cluster to vegetation changes, with high accuracy.

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 131A

Audiences: Everyone Is Invited

Contact: Lizsl De Leon