OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains

Kasaei, Hamidreza

doi:10.48550/arxiv.1902.03057

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…The DGFE module helps 3D convolutions hierarchically acquire global information, allowing the network to capture the contextual neighborhood of points. Despite using viewpoints in a predefined sequence, as opposed to any random views by DeepPano (Shi et al, 2015 ), Gan classifier (Varga et al, 2020 ), GPSP-DWRN (Long et al, 2021 ), OrthographicNet (Kasaei, 2019 ), PANORAMA-NN (Sfikas et al, 2017 ), and SeqViews2SeqLabels (Han et al, 2019 ) both of which are multi-view techniques, the method outperforms these approaches, making it suitable for high resolution input. The proposed method also outperforms PolyNet (Yavartanoo et al, 2021 ), a mesh-based 3D representation network that combined the features in a much smaller dimension using PolyShape's multi-resolution structure.…”

Section: Methodsmentioning

confidence: 99%

An improved fused feature residual network for 3D point cloud data

Gezawa,

Liu,

Jia

et al. 2023

Front. Comput. Neurosci.

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Point clouds have evolved into one of the most important data formats for 3D representation. It is becoming more popular as a result of the increasing affordability of acquisition equipment and growing usage in a variety of fields. Volumetric grid-based approaches are among the most successful models for processing point clouds because they fully preserve data granularity while additionally making use of point dependency. However, using lower order local estimate functions to close 3D objects, such as the piece-wise constant function, necessitated the use of a high-resolution grid in order to capture detailed features that demanded vast computational resources. This study proposes an improved fused feature network as well as a comprehensive framework for solving shape classification and segmentation tasks using a two-branch technique and feature learning. We begin by designing a feature encoding network with two distinct building blocks: layer skips within, batch normalization (BN), and rectified linear units (ReLU) in between. The purpose of using layer skips is to have fewer layers to propagate across, which will speed up the learning process and lower the effect of gradients vanishing. Furthermore, we develop a robust grid feature extraction module that consists of multiple convolution blocks accompanied by max-pooling to represent a hierarchical representation and extract features from an input grid. We overcome the grid size constraints by sampling a constant number of points in each grid using a simple K-points nearest neighbor (KNN) search, which aids in learning approximation functions in higher order. The proposed method outperforms or is comparable to state-of-the-art approaches in point cloud segmentation and classification tasks. In addition, a study of ablation is presented to show the effectiveness of the proposed method.

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Section: Methodsmentioning

confidence: 99%

An improved fused feature residual network for 3D point cloud data

Gezawa,

Liu,

Jia

et al. 2023

Front. Comput. Neurosci.

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“…In the continuation of this work, we will investigate the possibility of using deep transfer learning methods for 3D object recognition in open-ended domains. Some results obtained with a deep transfer learning approach have already been published [38].…”

Section: System Demonstrationmentioning

confidence: 99%

Interactive Open-Ended Object, Affordance and Grasp Learning for Robotic Manipulation

Kasaei

Shafii

Lopes

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Service robots are expected to autonomously and efficiently work in human-centric environments. For this type of robots, object perception and manipulation are challenging tasks due to need for accurate and real-time response. This paper presents an interactive open-ended learning approach to recognize multiple objects and their grasp affordances concurrently. This is an important contribution in the field of service robots since no matter how extensive the training data used for batch learning, a robot might always be confronted with an unknown object when operating in human-centric environments. The paper describes the system architecture and the learning and recognition capabilities. Grasp learning associates grasp configurations (i.e., end-effector positions and orientations) to grasp affordance categories. The grasp affordance category and the grasp configuration are taught through verbal and kinesthetic teaching, respectively. A Bayesian approach is adopted for learning and recognition of object categories and an instance-based approach is used for learning and recognition of affordance categories. An extensive set of experiments has been performed to assess the performance of the proposed approach regarding recognition accuracy, scalability and grasp success rate on challenging datasets and real-world scenarios.All authors are with IEETA -University of Aveiro, 3810-193, Aveiro, Portugal. S. Hamidreza Kasaei is also with

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OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains

Cited by 2 publications

References 28 publications

An improved fused feature residual network for 3D point cloud data

An improved fused feature residual network for 3D point cloud data

Interactive Open-Ended Object, Affordance and Grasp Learning for Robotic Manipulation

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