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In this paper, we propose a 3D object recognition approach, based on the shape distribution D2 and artificial neural networks. The challenge is to discriminate between similar and dissimilar shapes by finding a shape signature that can be constructed and classified quickly. We propose a connectionist system to recognize 3D objects in VRML (Virtual Reality Modeling Language) format. The key idea is to represent the signature of an object as a shape distribution sampled from a shape functiondoi:10.14419/ijet.v7i2.13.11620 fatcat:scp4baeblbbjhneberkp4pvtpu