Bridging Formal Shape Models and Deep Learning: A Novel Fusion for Understanding 3D Objects.

This article describes a novel fusion of a generative formal model for three-dimensional (3D) shapes with deep learning (DL) methods to understand the geometric structure of 3D objects and the relationships between their components, given a collection of unorganized point cloud measurements. Formal 3D shape models are implemented as shape grammar programs written in Procedural Shape Modeling Language (PSML). Users write PSML programs to describe complex objects, and DL networks estimate the configured free parameters of the program to generate 3D shapes. Users write PSML programs to enforce fundamental rules that define an object class and encode object attributes, including shapes, components, size, position, etc., into a parametric representation of objects. This fusion of the generative model with DL offers artificial intelligence (AI) models an opportunity to better understand the geometric organization of objects in terms of their components and their relationships to other objects. This approach allows human-in-the-loop control over DL estimates by specifying lists of candidate objects, the shape variations that each object can exhibit, and the level of detail or, equivalently, dimension of the latent representation of the shape. The results demonstrate the advantages of the proposed method over competing approaches.

UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications.

This article presents an analysis of current state-of-the-art sensors and how these sensors work with several mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on low-altitude and high-speed scenarios. A new experimental construct is created using highly realistic environments made possible by integrating the AirSim simulator with Google 3D maps models using the Cesium Tiles plugin. Experiments are conducted in this high-realism simulated environment to evaluate the performance of three distinct mapping algorithms: (1) Direct Sparse Odometry (DSO), (2) Stereo DSO (SDSO), and (3) DSO Lite (DSOL). Experimental results evaluate algorithms based on their measured geometric accuracy and computational speed. The results provide valuable insights into the strengths and limitations of each algorithm. Findings quantify compromises in UAV algorithm selection, allowing researchers to find the mapping solution best suited to their application, which often requires a compromise between computational performance and the density and accuracy of geometric map estimates. Results indicate that for UAVs with restrictive computing resources, DSOL is the best option. For systems with payload capacity and modest compute resources, SDSO is the best option. If only one camera is available, DSO is the option to choose for applications that require dense mapping results.

ASB Clinical Biomechanics Award Paper 2010 Virtual pre-operative reconstruction planning for comminuted articular fractures.

BACKGROUND: Highly comminuted intra-articular fractures are complex and difficult injuries to treat. Once emergent care is rendered, the definitive treatment objective is to restore the original anatomy while minimizing surgically induced trauma. Operations that use limited or percutaneous approaches help preserve tissue vitality, but reduced visibility makes reconstruction more difficult. A pre-operative plan of how comminuted fragments would best be re-positioned to restore anatomy helps in executing a successful reduction. METHODS: In this study, the methods for virtually reconstructing a tibial plafond fracture were developed and applied to clinical cases. Building upon previous benchtop work, novel image analysis techniques and puzzle solving algorithms were developed for clinical application. Specialty image analysis tools were used to segment the fracture fragment geometries from CT data. The original anatomy was then restored by matching fragment native (periosteal and subchondral) bone surfaces to an intact template, generated from the uninjured contralateral limb. FINDINGS: Virtual reconstructions obtained for ten tibial plafond fracture cases had average alignment errors of 0.39 (0.5 standard deviation) mm. In addition to precise reduction planning, 3D puzzle solutions can help identify articular deformities and bone loss. INTERPRETATION: The results from this study indicate that 3D puzzle solving provides a powerful new tool for planning the surgical reconstruction of comminuted articular fractures.

A computational/experimental platform for investigating three-dimensional puzzle solving of comminuted articular fractures.

Reconstructing highly comminuted articular fractures poses a difficult surgical challenge, akin to solving a complicated three-dimensional (3D) puzzle. Preoperative planning using computed tomography (CT) is critically important, given the desirability of less invasive surgical approaches. The goal of this work is to advance 3D puzzle-solving methods towards use as a preoperative tool for reconstructing these complex fractures. A methodology for generating typical fragmentation/dispersal patterns was developed. Five identical replicas of human distal tibia anatomy were machined from blocks of high-density polyetherurethane foam (bone fragmentation surrogate), and were fractured using an instrumented drop tower. Pre- and post-fracture geometries were obtained using laser scans and CT. A semi-automatic virtual reconstruction computer program aligned fragment native (non-fracture) surfaces to a pre-fracture template. The tibiae were precisely reconstructed with alignment accuracies ranging from 0.03 to 0.4 mm. This novel technology has the potential to significantly enhance surgical techniques for reconstructing comminuted intra-articular fractures, as illustrated for a representative clinical case.