Bertram Drost

Model globally, match locally: Efficient and robust 3D object recognition

This paper addresses the problem of recognizing free-form 3D objects in point clouds. Compared to traditional approaches based on point descriptors, which depend on local information around points, we propose a novel method that creates a global model description based on oriented point pair features and matches that model locally using a fast voting scheme. The global model description consists of all model point pair features and represents a mapping from the point pair feature space to the model, where similar features on the model are grouped together. Such representation allows using much sparser object and scene point clouds, resulting in very fast performance. Recognition is done locally using an efficient voting scheme on a reduced two-dimensional search space. We demonstrate the efficiency of our approach and show its high recognition performance in the case of noise, clutter and partial occlusions. Compared to state of the art approaches we achieve better recognition rates, and demonstrate that with a slight or even no sacrifice of the recognition performance our method is much faster then the current state of the art approaches.

3D Object Detection and Localization Using Multimodal Point Pair Features

Object detection and localization is a crucial step for inspection and manipulation tasks in robotic and industrial applications. We present an object detection and localization scheme for 3D objects that combines intensity and depth data. A novel multimodal, scale- and rotation-invariant feature is used to simultaneously describe the objects silhouette and surface appearance. The objects position is determined by matching scene and model features via a Hough-like local voting scheme. The proposed method is quantitatively and qualitatively evaluated on a large number of real sequences, proving that it is generic and highly robust to occlusions and clutter. Comparisons with state of the art methods demonstrate comparable results and higher robustness with respect to occlusions.

Local Hough Transform for 3D Primitive Detection

Detecting primitive geometric shapes, such as cylinders, planes and spheres, in 3D point clouds is an important building block for many high-level vision tasks. One approach for this detection is the Hough Transform, where features vote for parameters that explain them. However, as the voting space grows exponentially with the number of parameters, a full voting scheme quickly becomes impractical. Solutions in the literature, such as decomposing the global voting space, often degrade the robustness w.r.t. Noise, clutter or multiple primitive instances. We instead propose a local Hough Transform, which votes on sub-manifolds of the original parameter space. For this, only those parameters are considered that align a given scene point with the primitive. The voting then recovers the locally best fitting primitive. This local detection scheme is embedded in a coarse-to-fine detection pipeline, which refines the found candidates and removes duplicates. The evaluation shows high robustness against clutter and noise, as well as competitive results w.r.t. Prior art.

A performance evaluation of point pair features

Abstract More than a decade ago, the point pair features (PPFs) were introduced, showing a great potential for 3D object detection and pose estimation under very different conditions. Many modifications have been made to the original PPF, in each case showing varying degrees of improvement for specific datasets. However, to the best of our knowledge, no comprehensive evaluation of these features has been made. In this work, we evaluate PPFs on a large set of 3D scenes. We not only compare PPFs to local point cloud descriptors, but also investigate the internal variations of PPFs (different types of relations between two points). Our comparison is made on 7 publicly available datasets, showing variations on a number of parameters, e.g. acquisition technique, the number of objects/scenes and the amount of occlusion and clutter. We evaluate feature performance both at a point-wise object-scene correspondence level and for overall object detection and pose estimation in a RANSAC pipeline. Additionally, we also present object detection and pose estimation results for the original, voting based, PPF algorithm. Our results show that in general PPF is the top performer, however, there are datasets, which have low resolution data, where local histogram features show a higher performance than PPFs. We also found that PPFs compared to most local histogram features degrade faster under disturbances such as occlusion and clutter, however, PPFs still remain more descriptive on an absolute scale. The main contribution of this paper is a detailed analysis of PPFs, which highlights under which conditions PPFs perform particularly well as well as its main weaknesses.

A Hierarchical Voxel Hash for Fast 3D Nearest Neighbor Lookup

We propose a data structure for finding the exact nearest neighbors in 3D in approximately O(log(log(N)) time. In contrast to standard approaches such as k-d-trees, the query time is independent of the location of the query point and the distribution of the data set. The method uses a hierarchical voxel approximation of the data point’s Voronoi cells. This avoids backtracking during the query phase, which is a typical action for tree-based methods such as k-d-trees. In addition, voxels are stored in a hash table and a bisection on the voxel level is used to find the leaf voxel containing the query point. This is asymptotically faster than letting the query point fall down the tree. The experiments show the method’s high performance compared to state-of-the-art approaches even for large point sets, independent of data and query set distributions, and illustrates its advantage in real-world applications.

Graph-Based Deformable 3D Object Matching

We present a method for efficient detection of deformed 3D objects in 3D point clouds that can handle large amounts of clutter, noise, and occlusion. The method generalizes well to different object classes and does not require an explicit deformation model. Instead, deformations are learned based on a few registered deformed object instances. The approach builds upon graph matching to find correspondences between scene and model points. The robustness is increased through a parametrization where each graph vertex represents a full rigid transformation. We speed up the matching through greedy multi-step graph pruning and a constant-time feature matching. Quantitative and qualitative experiments demonstrate that our method is robust, efficient, able to detect rigid and non-rigid objects and exceeds state of the art.

Almost constant-time 3D nearest-neighbor lookup using implicit octrees

A recurring problem in 3D applications is nearest-neighbor lookups in 3D point clouds. In this work, a novel method for exact and approximate 3D nearest-neighbor lookups is proposed that allows lookup times that are, contrary to previous approaches, nearly independent of the distribution of data and query points, allowing to use the method in real-time scenarios. The lookup times of the proposed method outperform prior art sometimes by several orders of magnitude. This speedup is bought at the price of increased costs for creating the indexing structure, which, however, can typically be done in an offline phase. Additionally, an approximate variant of the method is proposed that significantly reduces the time required for data structure creation and further improves lookup times, outperforming all other methods and yielding almost constant lookup times. The method is based on a recursive spatial subdivision using an octree that uses the underlying Voronoi tessellation as splitting criteria, thus avoiding potentially expensive backtracking. The resulting octree is represented implicitly using a hash table, which allows finding the leaf node a query point belongs to with a runtime that is logarithmic in the tree depth. The method is also trivially extendable to 2D nearest neighbor lookups.