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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Angle Assessment for Upper Limb Rehabilitation: A Novel Light Detection and Ranging (LiDAR)-Based Approach

The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost–benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.

Klein, Luan C. (ORCID:000000016306574X)↗
DOE OSTI2024

Mono-Ether and Alcohol Bioblendstocks to Reduce the Fuel Penalty of Mixing Controlled Compression Ignition (MCCI) Engine Aftertreatment

An integrated approach utilizing catalysis experiments, process systems engineering, fuel property modeling, and engine testing was utilized in this project to optimize the production process and composition of a #2 diesel bioblendstock produced from ethanol consisting primarily of long-chain mono-ethers. The primary objective of the project was to determine the composition and to design the production process for a bioblendstock for #2 diesel fuel with > 50% reduction in greenhouse gas emissions relative to conventional diesel fuel. The desired bioblendstock needed to be blendable with #2 diesel fuel at > 5 vol. % while still meeting ASTM D975 diesel fuel specification properties and achieving improvements in fuel properties: increased cetane number, decreased sooting, and reduced pour point and cloud point temperatures. At the same time, it needed to reduce the fuel energy penalty associated with MCCI engine aftertreatment resulting in improved system efficiency. The findings of the current project demonstrate that primary objective of the work has been met, i.e., to determine the composition and to design the production process for a bioblendstock for #2 diesel fuel with > 50% reduction in greenhouse gas emissions relative to conventional diesel fuel. Additionally, the results indicate that the property objectives (increased cetane number, reduced pour and cloud points, and reduced sooting propensity) for the designed bioblendstock composition have also been met. Engine testing performed has also confirmed that the increased reactivity of the bioblendstock can be used to improve catalyst heating operation and to reduce the fuel penalty associated with this operation mode. The fuel property results demonstrate that >5 vol.% blending is easily achieved while meeting the ASTM D975 #2 diesel fuel property specifications tested in this work, as this was achieved for a blend with 43 vol. % of the bioblendstock. The results provide a foundation for future work to scaleup the catalytic production process designed in this work, with many of the challenges and areas for improvement being identified in this work to enable economic production with low GHG lifecycle emissions.

09 BIOMASS FUELS↗
DOE OSTI2024

Fuel property evaluation of unique fatty acid methyl esters containing β-hydroxy esters from engineered microorganisms

Unique fatty acid methyl esters (FAME) containing ..beta..-hydroxy esters were produced using an engineered microorganism by glucose fermentation. This study investigated the properties of the unique FAME mixture both neat and in blends with conventional diesel, as well as properties of ..beta..-hydroxy esters. The unique FAME blend contained relatively shorter-chain FAME (average fatty acid chain carbon number 14.6) with 58 % monounsaturated fatty acids and 9 % saturated and monounsaturated ..beta..-hydroxy acid chains. The unique FAME had significantly lower distillation T90 (321 °C versus 352 °C) and higher cetane number (56.7 versus 52) compared to soy biodiesel. Cloud points were within method repeatability. Unexpectedly (because of the lack of methylene-interrupted double bonds), the unique FAME had low oxidation stability (1.5 h) as determined by Rancimat induction period. Stability could be improved through addition of commonly used antioxidants. We speculate that monounsaturated ..beta..-hydroxy FAME may be the source of this instability. Blends with conventional diesel up to 50 vol% showed similar kinematic viscosity (within method repeatability) as blends of conventional FAME. The unique FAME had no effect on distillation T90 even at the 80% blend level. A 30 vol% blend into conventional diesel had a Rancimat induction period of only 2 h, very nearly the same as the neat unique FAME sample. The addition of antioxidants produced blends of acceptable stability. Based on an assessment of the properties of individual ..beta..-hydroxy FAME molecules, they have higher boiling point, higher cloud point, lower cetane number, and potentially lower storage stability than analogous FAME not having the ..beta..-hydroxy group. Removing them from the fuel product in the production process may result in a biodiesel product with superior properties to what is on the market today.

09 BIOMASS FUELS↗
DOE OSTI2021

Quality control and crop characterization framework for multi-temporal UAV LiDAR data over mechanized agricultural fields

Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Finally, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluation of Techniques for Vegetation Removal from UAV-Based Photogrammetric DSM near WIPP Land Withdrawal Act Boundary, NM

The Waste Isolation Pilot Plant is located within New Mexico's karst landscape and is the United States' only deep geologic radioactive waste repository which isolates transuranic waste from defense activities underground in a bedded salt formation. Characteristic karst topography in this region is made up of material (such as limestone) that can be dissolved by water over a long period of time, and includes features such as springs, surface streams, sinking streams, caves, and sinkholes. Karst features can potentially impact the integrity of the waste repository in the future and compromise WIPP's performance, particularly when coupled with incompatible land-use activities within the Land Withdrawal Act (LWA) boundary as well as increased water withdrawals outside the LWA. In order to better capture the ground surface topography to support hydrology modeling efforts, the U.S. DOE has enlisted FIU to develop a high-resolution digital elevation model (DEM). In a previous field study, a high-resolution digital surface model (DSM) of an adjacent representative site, Basin 6, was developed by photogrammetry using aerial images captured with unmanned aerial vehicles. The DSM generated was based on point clouds representing the entire landscapes, including points of terrain, vegetation and infrastructure. These above-ground features need to be removed to create a DSM/DEM with good representation of the bare ground. In recent years, many techniques including both pre- and post-processing of DSMs, were developed for vegetation removal with varied success. This research focuses on applying and comparing various vegetation removal methods using point clouds generated of the Basin 6 pilot study area. The 3D RGB-based point cloud classification using Python scripts was a modification of the procedure by Themistocleous (2019). The method using Liblas classifies the point cloud three-dimensionally, whereas the method by Themistocleous (2019) is two-dimensional. The preliminary result of the method using Liblas, however, still had a large amount of vegetation remaining compared to the Pix4D Machine Learning method. The vegetation layer remains highly visible in the DEMs processed using the vegetation indices as opposed to the Pix4D Machine Learning method, which produces a DEM where mostly bare ground is seen. Further modifications to the input parameters are required to improve the vegetation vs. bare ground classification using this approach. The next step will be to improve the method using Liblas and to also test other vegetation removal methods found in the literature review to compare them against the two described here.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Clutter Assessment for an Autonomous Multi-Agent Search Mission

This paper presents a method for evaluating the amount of clutter in a region where autonomous vehicles in a multi-agent system must operate based on LIDAR point cloud measurements. The point cloud is used to generate an occupancy grid which is then projected onto a 2D plane of vehicle motion, constituting an image. A series of Gaussian radial basis functions (GRBFs) is created, each centered at an occupied pixel in a 2D image, and summed together to form the clutter field. The clutter field is a representation of the density and permeability of the space at each coordinate. The clutter field is then approximated such that iso-clutter contours are simple geometric objects so that intelligent machine assets can easily query the distance between them and any given point in an environment. In this way, agents are able to determine whether to enter into or steer away from areas of interest. Each vehicle has a clutter threshold representing the clutter value of the space in which it can safely maneuver. The iso-clutter contour corresponding to a vehicle’s clutter threshold is treated as the boundary of an obstacle to be avoided. A simulation is presented where a multi-agent system is tasked with persistent observation of a cluttered area. Each vehicle in the simulation has a different clutter threshold. The vehicles use a potential field-based guidance algorithm, and an allocation of vehicles to specific regions of the space emerges.

multi-agent↗
NASA NTRS2017

Imaging Systems for Size Measurements of Debrisat Fragments

The overall objective of the DebriSat project is to provide data to update existing standard spacecraft breakup models. One of the key sets of parameters used in these models is the physical dimensions of the fragments (i.e., length, average-cross sectional area, and volume). For the DebriSat project, only fragments with at least one dimension greater than 2 mm are collected and processed. Additionally, a significant portion of the fragments recovered from the impact test are needle-like and/or flat plate-like fragments where their heights are almost negligible in comparison to their other dimensions. As a result, two fragment size categories were defined: 2D objects and 3D objects. While measurement systems are commercially available, factors such as measurement rates, system adaptability, size characterization limitations and equipment costs presented significant challenges to the project and a decision was made to develop our own size characterization systems. The size characterization systems consist of two automated image systems, one referred to as the 3D imaging system and the other as the 2D imaging system. Which imaging system to use depends on the classification of the fragment being measured. Both imaging systems utilize point-and-shoot cameras for object image acquisition and create representative point clouds of the fragments. The 3D imaging system utilizes a space-carving algorithm to generate a 3D point cloud, while the 2D imaging system utilizes an edge detection algorithm to generate a 2D point cloud. From the point clouds, the three largest orthogonal dimensions are determined using a convex hull algorithm. For 3D objects, in addition to the three largest orthogonal dimensions, the volume is computed via an alpha-shape algorithm applied to the point clouds. The average cross-sectional area is also computed for 3D objects. Both imaging systems have automated size measurements (image acquisition and image processing) driven by the need to quickly and accurately measure tens of thousands of debris fragments. Moreover, the automated size measurement reduces potential fragment damage/mishandling and ability for accuracy and repeatability. As the fragment characterization progressed, it became evident that the imaging systems had to be revised. For example, an additional view was added to the 2D imaging system to capture the height of the 2D object. This paper presents the DebriSat project's imaging systems and calculation techniques in detail; from design and development to maturation. The experiences and challenges are also shared.

Shiotani, B.↗
NASA NTRSDate not supplied

Entwine Point Tiles for 3D Visualization and Querying of ICESat-2

Point Cloud data from non-optical sensors present challenges in scientific computing in both volume of data and files, even for cloud services environments. As part of the Multi-Mission Algorithm and Analysis Platform (MAAP), a joint open science platform for global biomass modelling, we’ve developed a cloud optimized workflow for using ATL08 (ICESat-2) data as a point cloud. For MAAP, the ATL08 data product is published as Entwine Point Tiles (EPT), allowing users to visualize and query the full extent of this collection interactively without pre-downloading, or preprocessing. The EPT format is a cloud-optimized point cloud data format which re-organizes points into a cloud friendly spatially indexed data structure. MAAP uses AWS S3 to store these point clouds and serves them over OGC specified APIs, 3DTiles for visualization, and WFS for querying. This workflow allows for interactive 3D visualizations in a web browser, including notebook environments and facilitates on the fly subsetting for interactive data exploration, all of which can be applied to other similar sensors.

Alex Mandel↗
DOE OSTI2025

Point cloud-based diffusion models for the Electron-Ion Collider

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We focus on the events at the future Electron-Ion Collider, but we expect that our results can be extended to proton-proton and heavy-ion collisions. Second, previous generative models often relied on image-based techniques. The sparsity of the data can negatively affect the fidelity and sampling time of the model. We address these issues using point clouds and a novel architecture combining edge creation with transformer modules called Point Edge Transformers. Third, we adapt the foundation model OmniLearn, to generate full collider events. This approach may indicate a transition toward adapting and fine-tuning foundation models for downstream tasks instead of training new models from scratch.

Araz, Jack Y. [Stony Brook Univ., NY (United State↗
DOE OSTI2020

Vision-Guided Automation and Assistance

Robotic automation and assistance is an excellent way to increase efficiency and safety when working in a potentially hazardous workspace. However, many traditional automation approaches rely on the precise placement of items to be manipulated. The scope of such automated tasks is also limited to the manipulation of one type of object since there is no way to distinguish between different objects. The addition of a vision pipeline, where robot vision is used to localize and recognize objects, can allow the robot to perform more complex tasks in a variable workspace. In my vision pipeline, a depth camera is used to generate a 3D point cloud of the workspace. Objects to be manipulated are isolated in the point cloud by filtering out shared surfaces and using Euclidean cluster extraction to group clusters of points. The locations of the objects are then estimated from the point cloud of the isolated objects. The localization of objects with robot vision allows for variable workspaces, reducing the overhead associated with requiring all objects to be precisely placed in the workspace. For object recognition, deep learning is used to train a neural network that classifies objects from the raw point clouds. More complex tasks that require multiple objects to be manipulated differently can be achieved through the vision pipeline. Through its ability to visualize the workspace, robot vision greatly improves efficiency and safety of automated and assistive tasks.

42 ENGINEERING↗
DOE OSTI2021

Geometrical defect detection for additive manufacturing with machine learning models

This study proposed a scheme based on Machine Learning (ML) models to detect geometric defects of additively manufactured objects. The ML models are trained with synthetic 3D point clouds with defects and then applied to detect defects in actual production. Using synthetic 3D point clouds rather than experimental data could save a huge amount of training time and costs associated with many prints for each design. Besides distance differences of individual points between source and target point clouds, this scheme uses a new concept called “patch” to capture macro-level information about nearby points for ML training and implementation. Numerical comparisons of prediction results on experimental data with different shapes showed that the proposed scheme outperformed the existing Z-difference method in the literature. Five ML methods (Bagging of Trees, Gradient Boosting, Random Forest, K-nearest Neighbors and Linear Supported Vector Machine) were compared under various conditions, such as different point cloud densities and defect sizes. Bagging and Random Forest were found the two best models regarding predictability; and the right patch size was found to be at 20. The proposed ML-based scheme is applicable to in-situ defect detection during additive manufacturing with the aid of a proper 3D data acquisition system.

Additive manufacturing↗
DOE OSTI2020

Superior performance biodiesel from biomass-derived fusel alcohols and low grade oils: Fatty acid fusel esters (FAFE)

We demonstrated production of a superior performance biodiesel referred to here as fatty acid fusel alcohol esters (FAFE) – by reacting fusel alcohols (isobutanol, 3-methyl-1-butanol, and (S)-(-)-2-methyl-1-butanol) with oil (glyceryl trioleate) using lipase from Aspergillus oryzae. Reaction conditions corresponding to a molar ratio of 5:1 (fusel alcohols to oil), enzyme loading of 2% w/w, reaction temperature of 35 °C, shaking speed of 250 rpm, and reaction time of 24 h achieved >97% conversion to FAFE. Further, FAFE obtained from reacting a fusel alcohol mixture with corn oil were evaluated for use as a fuel for diesel engines. FAFE mixtures showed superior combustion and cold-flow properties, with the derived cetane numbers up to 4.8 points higher, cloud points up to -6 °C lower, and the heat of combustion up to 2.1% higher than the corresponding FAME samples, depending on the fusel mixture used. This represents a significant improvement for all three metrics, which are typically anti-correlated. Finally, FAFE provides a new opportunity for expanded usage of biodiesel by addressing feedstock limitations, fuel performance, and low temperature tolerance.

09 BIOMASS FUELS↗
DOE OSTI2020

Autonomous Radiation Mapping and Quantification using an Unmanned Ground Vehicle. Part I - Environment Mapping

When characterizing facilities, advanced autonomous systems are safe, efficient, and cost-effective tools, which can safely deploy state-of-the-art instrumentation without exposing workers to radiation risks. One main focus of the research is the visual mapping of the environment.This paper presents a framework for the mapping aspect that utilizes lidar and SLAM technologies coupled with a monocular camera to create a point cloud of the environment. Upon completion, the present research work aims to provide an autonomous mobile robot the capability to localize itself and aid the user with visualizing the radiation data. Challenges: C++ node needs to properly synchronize image and point cloud data. 3D lidar has resolution of ± 3 cm. Proper camera calibration needed. Transforms need to be precise for proper alignment of image and point-cloud. Results: Voxelated point clouds. Accurate colors from calibrated cameras including details smaller than lidar resolution. Developed Robotics Operating System (ROS) package for ease of transfer to other robot platforms.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Standoff inspection using geometry-informed full-wavefield response measurements

A vibrometer may measure acoustic responses in portions of a structure along a scan path to acoustic excitation of the structure. A ranging device may measure distances to the portions of the structure along the scan path. A three-dimensional point cloud may be generated based on the acoustic responses in the portions of the structure and the distances to the portions of the structure. The three-dimensional point cloud may include points representing geometry of the portions of the structure. The points may be associated with the acoustic responses in corresponding portions of the structure. One or more properties of the structure may be determined based on an analysis of the three-dimensional point cloud.

Flynn, Eric Brian↗
DOE OSTI2021

Experimental Investigation of Polymer Induced Fouling of Heater Tubes in the First-Ever Polymer Flood Pilot on Alaska North Slope

Mineral fouling in heat exchangers has been extensively investigated by researchers in recent times. The oil and gas industry has a long history of fouling issues in production systems as a result of produced fluids treatment. Due to decline in production rates in oilfields new technologies are being developed and field tested in pilots. Polymer flooding is one such technology that involves addition of polymers to injection fluids to enhance oil production. A polymer flood pilot has been set up in the Schrader Bluff viscous oil reservoir at Milne Point field on the Alaska North Slope (ANS). The results from the pilot are encouraging, however a major concern of the operator is the influence of polymer on the production system after breakthrough, especially the fouling in heat exchangers. This study investigates the propensity of polymer fouling on the heater tubes as a function of different variables, with the ultimate goal of determining safe and efficient operating conditions. This work applies a multi-experimental approach to study the severity of polymer-induced fouling in both dynamic and static states of produced fluids as well as studying the stability of polymer solutions at different temperatures. A unique experimental setup was designed and developed in-house to simulate the fouling process on the heating tube. The influence of heating tube skin temperature, tube material, and polymer concentration on fouling tendency was investigated. Each test was run five times with the same tube, and in each run, the freshly prepared synthetic brine and polymer solution was heated from 77°F to 122°F to mimic field-operating conditions. The heating time and fouling amount were recorded for each run. Dynamic Scale Loop (DSL) tests were conducted to study fouling due to polymer at different temperatures (165°F to 350°F) in a dynamic state of fluid flow where the fluids mimic the residence time of fluids in the heat exchanger on the field pilot. Cloud point measurement has also been conducted to find the critical temperature at which the polymer in solution becomes unstable and precipitates out. The morphology and composition of the deposit samples were analyzed by environmental scanning electron microscopy (ESEM) and X-ray diffraction (XRD), respectively. It was found that the presence of polymer in produced fluids would aggravate the fouling issues on both carbon steel and stainless-steel surfaces at all tested skin temperatures. Only higher skin temperatures of 250°F and 350°F could cause polymer-induced fouling issues on the copper tube surface, and the fouling tendency increased with polymer concentration. At the lower skin temperatures of 165°F, no polymer-induced fouling was identified on the copper tube. A critical temperature that is related to the cloud point of the polymer solution was believed to exist, below which polymer-induced fouling would not occur, and only mineral scale was deposited but above which the polymer would aggravate the fouling issue. The cloud point of the tested polymer solution was determined to be between 220°F and 230°F. In the DSL tests it was found that at higher skin temperatures of 250°F and 350°F tube blocking was observed in the DSL tests whereas the tests at 165°F and 200°F did not show any tube blocking in the same time period. These experiments also manifested the influence of cloud point of the solution as deposit rate increased significantly in both carbon steel and stainless-steel tubes when the skin temperature was higher than the solution cloud point. The results of this study have provided guidance to the operator for the field-operations.

Dhaliwal, Anshul↗
NASA NTRS1993

Atmospheric correction of AVIRIS data of Monterey Bay contaminated by thin cirrus clouds

Point source measurements (e.g. sun photometer data, weather station observations) are often used to constrain radiative transfer models such as MODTRAN/LOWTRAN7 when atmospherically correcting AVIRIS imagery. The basic assumption is that the atmosphere is horizontally homogeneous throughout the entire area. If the target area of interest is isolated a distance away from the point measurement position, the calculated visibility and atmospheric profiles may not be characteristic of the atmosphere over the target. AVIRIS scenes are often rejected when cloud cover exceeds 10%. However, if the cloud cover is determined to be primarily cirrus rather than cumulus, in-water optical properties may still be extracted over open ocean. High altitude cirrus clouds are non-absorbing at 744 nm. If the optical properties of the AVIRIS scene can be determined from the 744 nm band itself, the atmospheric conditions during the overflight may be deduced.

Vandenbosch, Jeannette↗
DOE OSTI2024

Synthesizing realistic sand assemblies with denoising diffusion in latent space

Abstract The shapes and morphological features of grains in sand assemblies have far‐reaching implications in many engineering applications, such as geotechnical engineering, computer animations, petroleum engineering, and concentrated solar power. Yet, our understanding of the influence of grain geometries on macroscopic response is often only qualitative, due to the limited availability of high‐quality 3D grain geometry data. In this paper, we introduce a denoising diffusion algorithm that uses a set of point clouds collected from the surface of individual sand grains to generate grains in the latent space. By employing a point cloud autoencoder, the three‐dimensional point cloud structures of sand grains are first encoded into a lower‐dimensional latent space. A generative denoising diffusion probabilistic model is trained to produce synthetic sand that maximizes the log‐likelihood of the generated samples belonging to the original data distribution measured by a Kullback‐Leibler divergence. Numerical experiments suggest that the proposed method is capable of generating realistic grains with morphology, shapes and sizes consistent with the training data inferred from an F50 sand database. We then use a rigid contact dynamic simulator to pour the synthetic sand in a confined volume to form granular assemblies in a static equilibrium state with targeted distribution properties. To ensure third‐party validation, 50,000 synthetic sand grains and the 1542 real synchrotron microcomputed tomography (SMT) scans of the F50 sand, as well as the granular assemblies composed of synthetic sand grains are made available in an open‐source repository.

Vlassis, Nikolaos N.↗
DOE OSTI2021

Concentric Spherical GNN for 3D Representation Learning

Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry. We propose a novel multi-resolution convolutional architecture for learning over concentric spherical feature maps, of which the single sphere representation is a special case. Our hierarchical architecture is based on alternatively learning to incorporate both intra-sphere and inter-sphere information. We show the applicability of our method for two different types of 3D inputs, mesh objects, which can be regularly sampled, and point clouds, which are irregularly distributed. We also propose an efficient mapping of point clouds to concentric spherical images, thereby bridging spherical convolutions on grids with general point clouds. We demonstrate the effectiveness of our approach in improving state-of-the-art performance on 3D classification tasks with rotated data.

97 MATHEMATICS AND COMPUTING↗