Mian Ajmal S, Bennamoun Mohammed, Owens Robyn
School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia.
IEEE Trans Pattern Anal Mach Intell. 2006 Oct;28(10):1584-601. doi: 10.1109/TPAMI.2006.213.
Viewpoint independent recognition of free-form objects and their segmentation in the presence of clutter and occlusions is a challenging task. We present a novel 3D model-based algorithm which performs this task automatically and efficiently. A 3D model of an object is automatically constructed offline from its multiple unordered range images (views). These views are converted into multidimensional table representations (which we refer to as tensors). Correspondences are automatically established between these views by simultaneously matching the tensors of a view with those of the remaining views using a hash table-based voting scheme. This results in a graph of relative transformations used to register the views before they are integrated into a seamless 3D model. These models and their tensor representations constitute the model library. During online recognition, a tensor from the scene is simultaneously matched with those in the library by casting votes. Similarity measures are calculated for the model tensors which receive the most votes. The model with the highest similarity is transformed to the scene and, if it aligns accurately with an object in the scene, that object is declared as recognized and is segmented. This process is repeated until the scene is completely segmented. Experiments were performed on real and synthetic data comprised of 55 models and 610 scenes and an overall recognition rate of 95 percent was achieved. Comparison with the spin images revealed that our algorithm is superior in terms of recognition rate and efficiency.
在存在杂乱和遮挡的情况下，对自由形式物体进行视点无关的识别及其分割是一项具有挑战性的任务。我们提出了一种新颖的基于3D模型的算法，该算法能自动且高效地执行此任务。物体的3D模型是通过其多个无序距离图像（视图）离线自动构建的。这些视图被转换为多维表格表示（我们称之为张量）。通过使用基于哈希表的投票方案，将一个视图的张量与其余视图的张量同时进行匹配，从而在这些视图之间自动建立对应关系。这会生成一个相对变换图，用于在将视图集成到无缝3D模型之前对其进行配准。这些模型及其张量表示构成了模型库。在在线识别过程中，通过投票将场景中的一个张量与库中的张量同时进行匹配。为获得最多投票的模型张量计算相似性度量。具有最高相似性的模型被变换到场景中，如果它与场景中的一个物体精确对齐，则该物体被宣布为已识别并被分割。重复此过程，直到场景被完全分割。对由55个模型和610个场景组成的真实和合成数据进行了实验，实现了95%的总体识别率。与自旋图像的比较表明，我们的算法在识别率和效率方面更具优势。
