Recognizing objects in 3D point clouds with multi-scale local features.
Recognizing objects in 3D point clouds with multi-scale local features.
复制标题
使用多尺度局部特征识别 3D 点云中的对象
DOI：
10.3390/s141224156
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发表时间：
2014-12-15
期刊：
影响因子：
--
通讯作者：
Lei Y
中科院分区：
文献类型：
--
作者：
Lu M;Guo Y;Zhang J;Ma Y;Lei Y
在存在大量杂波和遮挡的情况下，从点云中识别3D对象是一项极具挑战性的任务。本文提出了一种由粗到精的三维物体识别算法。在离线训练阶段，每个模型用一组多尺度局部表面特征表示。在在线识别阶段，首先从每个场景中检测一组关键点。这些关键点周围的局部曲面进一步使用多尺度特征描述符进行编码。然后，将这些场景特征与所有模型特征进行匹配，以生成识别假设，其中包括模型假设和姿势假设。最后，对这些假设进行验证，得到识别结果。提出的算法在两个标准数据集上进行了测试，并与最先进的算法进行了严格的比较。实验结果表明，该算法具有全自动、高效率的特点。它对遮挡和杂波也具有很强的鲁棒性。它在所有这些数据集上取得了最好的识别性能，与现有算法相比显示了其优越性。
Recognizing 3D objects from point clouds in the presence of significant clutter and occlusion is a highly challenging task. In this paper, we present a coarse-to-fine 3D object recognition algorithm. During the phase of offline training, each model is represented with a set of multi-scale local surface features. During the phase of online recognition, a set of keypoints are first detected from each scene. The local surfaces around these keypoints are further encoded with multi-scale feature descriptors. These scene features are then matched against all model features to generate recognition hypotheses, which include model hypotheses and pose hypotheses. Finally, these hypotheses are verified to produce recognition results. The proposed algorithm was tested on two standard datasets, with rigorous comparisons to the state-of-the-art algorithms. Experimental results show that our algorithm was fully automatic and highly effective. It was also very robust to occlusion and clutter. It achieved the best recognition performance on all of these datasets, showing its superiority compared to existing algorithms.
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DOI：
10.1023/b:visi.0000029664.99615.94
发表时间：
2004-11-01
影响因子：
19.5
作者：
Lowe, DG
通讯作者：
Lowe, DG
DOI：
10.3390/s140609471
发表时间：
2014-05-27
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Cahalane C;McElhinney CP;Lewis P;McCarthy T
通讯作者：
McCarthy T
影响因子：
6.2
作者：
Osada, R;Funkhouser, T;Dobkin, D
通讯作者：
Dobkin, D
DOI：
10.1109/tpami.2005.188
发表时间：
2005-10-01
影响因子：
23.6
作者：
Mikolajczyk, K;Schmid, C
通讯作者：
Schmid, C
DOI：
10.1109/34.121791
发表时间：
1992-02-01
影响因子：
23.6
作者：
BESL, PJ;MCKAY, ND
通讯作者：
MCKAY, ND