G. Seetharaman, and
IEEE Applied Imagery Pattern Recognition Workshop (AIPR),
In this paper we evaluate the quality of feature detection and 3D reconstruction on a Wide Area Motion Imagery (WAMI) sequence with increasing JPEG compression ratio. Feature detection is critical for computer vision tasks such as 3D reconstruction. For some 3D reconstruction approaches, the quality of a 3D model relies upon consistent detection of the same feature points over consecutive frames in an image sequence. Since the performance of feature detectors is highly sensitive to compression artifacts, we evaluate the influence of image quality on feature detection accuracy. Many datasets (e.g. WAMI) use JPEG compression to decrease the data storage and network bandwidth utilization while attempting to preserve image quality by adaptively adjusting the compression ratio. Consequently, it is important to understand the impact of JPEG compression on the quality of feature detection in 2D space and the subsequent 3D reconstruction results. We design and perform two evaluation procedures on the WAMI sequence. We use structure tensor to detect feature points on an image sequence with increasing JPEG compression ratio (10:1, 15:1, 20:1, 30:1, 40:1, 100:1, and 150:1). Compression ratio of 10:1 is used as the baseline (groundtruth). First we compare the feature points from images of different qualities with the groundtruth features and evaluate them on pixel level in 2D space. After that, a 3D model in the form of point cloud is generated from each set of feature points and compared with the groundtruth point cloud. We provide quantitative and visualized results for the evaluation.
author = "K. Gao and S. Yao and H. AliAkbarpour and S. Agarwal and G. Seetharaman and K. Palaniappan",
title = "Sensitivity of multiview 3D point cloud reconstruction to compression quality and image feature detectability",
year = 2019,
journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
pages = "1-6",
month = "Oct",
keywords = "jpeg compression, feature detection, 3d reconstruction, evaluation",
doi = "10.1109/AIPR47015.2019.9174580",
url = "https://ieeexplore.ieee.org/document/9174580"
K. Gao, S. Yao, H. AliAkbarpour, S. Agarwal, G. Seetharaman, and K. Palaniappan. Sensitivity of multiview 3D point cloud reconstruction to compression quality and image feature detectability. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-6, October 2019.