Geometric Probing of Dense Range Data
Geometric Probing of Dense Range Data
复制标题
密集数据的几何探测
DOI：
10.1109/34.993557
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
M. Greenspan
中科院分区：
文献类型：
--
作者：
M. Greenspan
提出了一种新的方法，用于在密集距离图像数据中有效和可靠地确定三维物体的位姿。该方法是基于一个最小的几何探测策略，假设的对象与一些选定的图像点的交叉点，并搜索额外的表面数据的位置相对于该点。该策略在离散域中实现为二叉决策树分类器。树叶节点表示模型的个体体素模板，每个不同的模型姿势有一个模板。内部节点表示其后代叶节点的模板的联合。所有叶节点模板的并集是模型在其离散位姿空间上的完整模板集。每个内部节点还编码单个体素，这是其子节点模板中最常见的元素。遍历自由相当于有效地匹配所选图像种子位置处的大模板集。该方法的实施和广泛的实验进行了各种组合的树设计和遍历孤立，杂乱，和闭塞的场景条件下。结果表明，效率和可靠性之间的权衡。结果表明，存在树设计和遍历的组合，这是高效和可靠的。
A new method is presented for the efficient and reliable pose determination of 3D objects in dense range image data. The method is based upon a minimalistic Geometric Probing strategy that hypothesizes the intersection of the object with some selected image point, and searches for additional surface data at locations relative to that point. The strategy is implemented in the discrete domain as a binary decision tree classifier. The tree leaf nodes represent individual voxel templates of the model, with one template per distinct model pose. The internal nodes represent the union of the templates of their descendant leaf nodes. The union of all leaf node templates is the complete template set of the model over its discrete pose space. Each internal node also encodes a single voxel which is the most common element of its child node templates. Traversing the free is equivalent to efficiently matching the large set of templates at a selected image seed location. The method was implemented and extensive experiments were conducted for a variety of combinations of tree designs and traversals under isolated, cluttered, and occluded scene conditions. The results demonstrated a tradeoff between efficiency and reliability. It was concluded that there exist combinations of tree design and traversal which are both highly efficient and reliable.