TY - GEN
T1 - Adaptive Fusion-Based 3D Keypoint Detection for RGB Point Clouds
AU - Iqbal, Muhammad Zafar
AU - Bobkov, Dmytro
AU - Steinbach, Eckehard
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - We propose a novel keypoint detector for 3D RGB Point Clouds (PCs). The proposed keypoint detector exploits both the 3D structure and the RGB information of the PC data. Keypoint candidates are generated by computing the eigenvalues of the covariance matrix of the PC structure information. Additionally, from the RGB information, we estimate the salient points by an efficient adaptive difference of Gaussian-based operator. Finally, we fuse the resulting two sets of salient points to improve the repeatability of the 3D keypoint detector. The proposed algorithm is compared against the state-of-the-art algorithms on two benchmark datasets. The experimental results show that the proposed scheme outperforms the best existing method by 5.35% and 60.98 points on the SHOT-Kinect dataset and by 5.45% and 145.54 points on the SHOT-SpaceTime dataset in terms of relative and absolute repeatability, respectively.
AB - We propose a novel keypoint detector for 3D RGB Point Clouds (PCs). The proposed keypoint detector exploits both the 3D structure and the RGB information of the PC data. Keypoint candidates are generated by computing the eigenvalues of the covariance matrix of the PC structure information. Additionally, from the RGB information, we estimate the salient points by an efficient adaptive difference of Gaussian-based operator. Finally, we fuse the resulting two sets of salient points to improve the repeatability of the 3D keypoint detector. The proposed algorithm is compared against the state-of-the-art algorithms on two benchmark datasets. The experimental results show that the proposed scheme outperforms the best existing method by 5.35% and 60.98 points on the SHOT-Kinect dataset and by 5.45% and 145.54 points on the SHOT-SpaceTime dataset in terms of relative and absolute repeatability, respectively.
KW - 3d keypoint detector
KW - difference of Gaus-sian
KW - point cloud
KW - salient point
UR - http://www.scopus.com/inward/record.url?scp=85076810619&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2019.8803680
DO - 10.1109/ICIP.2019.8803680
M3 - Conference contribution
AN - SCOPUS:85076810619
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3711
EP - 3715
BT - 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PB - IEEE Computer Society
T2 - 26th IEEE International Conference on Image Processing, ICIP 2019
Y2 - 22 September 2019 through 25 September 2019
ER -