Octree-based Method Research Articles

A Storage-Saving Quadtree-Based Multibeam Bathymetry Map Representation Method

With the rapid advancement of the simultaneous localization and mapping (SLAM) technology, the collaboration of several autonomous underwater vehicles (AUVs) in large-scale seafloor imaging has become a trending topic. Electromagnetic waves are difficult to transfer underwater, the only viable method of communication is acoustic transmission, but its bandwidth is limited. Therefore, how to compress and process multibeam bathymetry maps so that AUVs can acquire maps gathered by other AUVs has become an important topic of study. This study presents a representation approach for multibeam bathymetry maps based on a quadtree structure. In comparison to the girding approach, the sparse pseudo-input Gaussian processes (SPGPs) method, and the octree-based method, the quadtree-based method suggested in this study preserves precision while compressing storage space. Experiments utilizing field data validate the performance of the proposed technique, and the method’s ability to compress storage space towards an AUV cooperative SLAM’s scenario.

Roof Plane Segmentation from Airborne LiDAR Data Using Hierarchical Clustering and Boundary Relabeling

The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics.

Electrospray molecular dynamics simulations using an octree-based Coulomb interaction method.

A new octree-based Coulomb interaction model is developed to model the electrospray of ionic liquids (ILs) in molecular dynamics. Using an octree-based method, Coulomb interactions are categorized as intra- and interleaf Coulomb interactions based on a criterion related to the Bjerrum length of the IL. The octree-based method is found capable of reproducing Coulomb energy in agreement with established and computationally more expensive models, such as the direct Coulomb and the damped shifted force (DSF) method in the absence of an external electric field. In the presence of an external electric field, the octree-based method produces distinctly different results compared to that obtained by the direct Coulomb method. The time required to form Taylor's cone was shorter for the octree method compared to the direct Coulomb approach. While no emission larger than monomers was observed from the direct Coulomb simulation, emission of larger species such as dimers and trimers was observed when the octree-based Coulomb interaction model was used. Furthermore, the octree-based model forms a smaller ion emission cone compared to that from the direct Coulomb method.

H-CNN: Spatial Hashing Based CNN for 3D Shape Analysis.

We present a novel spatial hashing based data structure to facilitate 3D shape analysis using convolutional neural networks (CNNs). Our method builds hierarchical hash tables for an input model under different resolutions that leverage the sparse occupancy of 3D shape boundary. Based on this data structure, we design two efficient GPU algorithms namely hash2col and col2hash so that the CNN operations like convolution and pooling can be efficiently parallelized. The perfect spatial hashing is employed as our spatial hashing scheme, which is not only free of hash collision but also nearly minimal so that our data structure is almost of the same size as the raw input. Compared with existing 3D CNN methods, our data structure significantly reduces the memory footprint during the CNN training. As the input geometry features are more compactly packed, CNN operations also run faster with our data structure. The experiment shows that, under the same network structure, our method yields comparable or better benchmark results compared with the state-of-the-art while it has only one-third memory consumption when under high resolutions (i.e., 2563).

An octree-based method for shape from inconsistent silhouettes

Shape-from-Silhouette (SfS) is the widely known problem of obtaining the 3D structure of an object from its silhouettes. Two main approaches can be employed: those based on voxel sets, which perform an exhaustive search of the working space, and those based on octrees, which perform a top-down analysis that speeds up the computation. The main problem of both approaches is the need for perfect silhouettes to obtain accurate results. Perfect background subtraction hardly ever happens in realistic scenarios, so these techniques are restricted to controlled environments where the consistency hypothesis can be assumed. Recently, some approaches (all of them based on voxel sets) have been proposed to solve the problem of inconsistency. Their main drawback is the high computational cost required to perform an exhaustive analysis of the working space. This paper proposes a novel approach to solve SfS with inconsistent silhouettes from an octree based perspective. The inconsistencies are dealt by means of the Dempster–Shafer (DS) theory and we employ a Butterworth function for adapting threshold values in each resolution level of the octree. The results obtained show that our proposal provides higher reconstruction quality than the standard octree based methods in realistic environments. When compared to voxel set approaches that manage inconsistency, our method obtains similar results with a reduction in the computing time of an order of magnitude.

Progressive lossless compression of volumetric data using small memory load

Nowadays, applications dealing with volumetric datasets, Medical applications being a typical representative, have become possible even on low cost computers due to a rapid increase of computer memory and processing power. However, even today, dealing with volumetric datasets creates two considerable problems: slow visualization and large file sizes. While recently, due to significant progress in graphics hardware, real-time or near real-time volume visualization has become possible, volume compression still remains a problematic issue. This paper introduces a new method for lossless compression of volumetric datasets. It is based on quadtree encoding. The method consists of three steps: during initialization, so-called division quadtree is built. The smallest unit of the division quadtree is called basic macro-block. During the processing phase, Boolean intersection is built on pairs of quadtrees, and the differences are stored. In the last phase, the variable length encoding is applied to reduce the entropy among the differences. Proposed method supports progressive visualization, what is especially important when a transfer trough the internet is needed. To test the efficiency of this method it was compared to popular octree encoding scheme. The results proved that data coherence is exploited more sufficiently using proposed quadtree approach. Additional advantage of this approach is that the algorithm does not need a lot of memory space. Only two quadtrees of two consecutive slices need be loaded in the memory at the same time. This feature makes this algorithm extremely attractive for possible hardware implementation. This paper introduces a new method for the compression of volumetric datasets. It is based on quadtree encoding. This method consists of three steps: during initialization, a so-called division quadtree is built. The smallest, unit of the division quadtree is called a basic macro-block. A Boolean intersection is built on pairs of quadtrees during the processing phase and the differences are stored. In the last phase, variable length encoding is applied to reduce entropy among the differences. This method has been compared with the popular octree-based method and gives, in general, better compression results. In addition, this method can be realized using small on-board memory.

Dual contouring of hermite data

This paper describes a new method for contouring a signed grid whose edges are tagged by Hermite data (i.e; exact intersection points and normals). This method avoids the need to explicitly identify and process "features" as required in previous Hermite contouring methods. Using a new, numerically stable representation for quadratic error functions, we develop an octree-based method for simplifying contours produced by this method. We next extend our contouring method to these simpli£ed octrees. This new method imposes no constraints on the octree (such as being a restricted octree) and requires no "crack patching". We conclude with a simple test for preserving the topology of the contour during simplification.

Parallel load balancing for dynamic execution environments

The development and implementation of a new method for parallel dynamic load balancing is presented. The load balancer uses an octree-based method to calculate the amount of imbalance and determine a new partitioning for the hybrid mesh. The balancer is designed to redistribute the work when the loads on the processors change due to local mesh adaptation or to changes in the parallel execution system. For the case of a dynamic parallel system, the loads on the processors are determined by run time measurements and the balancer redistributes the work based on these timings. Since the load balancer uses the same octree-based technique employed by the partitioner, the overall change in the partitions is minimized and a reduced amount of data migration is realized. The qualities of the partitions are maintained even after multiple load balancings. The effectiveness of the dynamic load balancer is demonstrated via parallel execution times for adaptive turbulent flow simulations.