Recognizing 3D objects in the presence of noise, varying mesh resolution, occlusion and clutter is a very challenging task. This paper presents a novel method named Rotational Projection Statistics (RoPS). It has three major modules: Local Reference Frame (LRF) definition, RoPS feature description and 3D object recognition. We propose a novel technique to define the LRF by calculating the scatter matrix of all points lying on the local surface. RoPS feature descriptors are obtained by rotationally projecting the neighboring points of a feature point onto 2D planes and calculating a set of statistics (including low-order central moments and entropy) of the distribution of these projected points. Using the proposed LRF and RoPS descriptor, we present a hierarchical 3D object recognition algorithm. The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets. Our proposed techniques exhibited superior performance compared to existing techniques. We also showed that our method is robust with re-

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3D object recognition in cluttered scenes is a rapidly growing research area. Based on the used types of features, 3D object recognition methods can broadly be divided into two categories-global or local feature based methods. Intensive research has been done on local surface feature based methods as they are more robust to occlusion and clutter which are frequently present in a real-world scene. This paper presents a comprehensive survey of existing local surface feature based 3D object recognition methods. These methods generally comprise three phases: 3D keypoint detection, local surface feature description, and surface matching. This paper covers an extensive literature survey of each phase of the process. It also enlists a number of popular and contemporary databases together with their relevant attributes.

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Abstract:To develop functional sustainable epoxy resins, we report a novel epoxy resin (DEU-EP) with high net biobased content (70.2 wt%) derived from renewable eugenol. We comparatively study DEU-EP with a commercial bisphenol A epoxy resin (DGEBA) in the presence of a most representative aromatic diamine curing agent, 4,4'-diaminodiphenyl methane (DDM).Differential scanning calorimetry reveals that DEU-EP can be sufficiently cured by DDM at a slower rate than DGEBA. By applying an autocatalytic reaction model, we adequately simulate the curing rate of DEU-EP/DDM, and reveal its detailed kinetic mechanisms from model-free isoconversional analysis. Dynamic mechanical analysis shows that DEU-EP/DDM takes higher storage modulus up to ~97 o C than DGEBA/DDM with the glass temperature of 114 o C. Nanoindentation and thermogravimetric analysis demonstrate that compared with DGEBA/DDM, DEU-EP/DDM exhibits a 20%, 6.7% and 111% increase in Young's modulus, hardness and char yield, respectively. Microscale combustion calorimetry data show that DEU-EP/DDM expresses 55% and 38% lower heat release rate and total heat release than DGEBA/DDM, respectively.Macroscopically, the horizontal burning test approves DEU-EP/DDM can self-extinguish in a short time. Our results demonstrate that the eugenol building blocks and their arrangement greatly affect the cure behaviors of DEU-EP/DDM, and contribute significantly to its enhanced mechanical properties, high-temperature charring ability and chain motions at glass state, as well as the reduced flammability. To summarize, DEU-EP exhibits a high promise as a new sustainable epoxy monomer for fabricating high biobased content, high rigid and low flammable epoxy materials.

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A novel bio-epoxy resin, TPEU-EP, was developed. It possesses good intrinsic flame retardancy, low smoke production, and excellent mechanical properties, showing high promise for application.