Deep learning of volumetric representation for 3D object recognition

Liu, Hongsen; Cong, Yang; Tang, Yandong

doi:10.1109/yac.2017.7967493

Cited by 4 publications

(1 citation statement)

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“…By utilizing metric space distances, the network can learn local features with increasing contextual scales. Liu et al by [24] suggested using volumetric representation and unsupervised deep network to obtain the features of point cloud data. They likewise applied the Hough Forest method on the gathered features and achieve object detection and position estimation concurrently.…”

Section: Related Workmentioning

confidence: 99%

VoxRec: Hybrid Convolutional Neural Network for Active 3D Object Recognition

Karambakhsh

Sheng

et al. 2020

IEEE Access

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Deep Neural Network methods have been used to a variety of challenges in automatic 3D recognition. Although discovered techniques provide many advantages in comparison with conventional methods, they still suffer from different drawbacks, e.g., a large number of pre-processing stages and timeconsuming training. In this paper, an innovative approach has been suggested for recognizing 3D models. It contains encoding 3D point clouds, surface normal, and surface curvature, merge them to provide more effective input data, and train it via a deep convolutional neural network on Shapenetcore dataset. We also proposed a similar method for 3D segmentation using Octree coding method. Finally, comparing the accuracy with some of the state-of-the-art demonstrates the effectiveness of our proposed method. INDEX TERMS Object recognition, recurrent neural networks, multi-layer neural network, octrees.

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Section: Related Workmentioning

confidence: 99%