Review of multi-view 3D object recognition methods based on deep learning
Abstract Three-dimensional (3D) object recognition is widely used in automated driving,
medical image analysis, virtual/augmented reality, artificial intelligence robots, and other …
medical image analysis, virtual/augmented reality, artificial intelligence robots, and other …
View-GCN: View-based graph convolutional network for 3D shape analysis
View-based approach that recognizes 3D shape through its projected 2D images has
achieved state-of-the-art results for 3D shape recognition. The major challenge for view …
achieved state-of-the-art results for 3D shape recognition. The major challenge for view …
A review on deep learning approaches for 3D data representations in retrieval and classifications
AS Gezawa, Y Zhang, Q Wang, L Yunqi - IEEE access, 2020 - ieeexplore.ieee.org
Deep learning approach has been used extensively in image analysis tasks. However,
implementing the methods in 3D data is a bit complex because most of the previously …
implementing the methods in 3D data is a bit complex because most of the previously …
Self-supervised deep learning on point clouds by reconstructing space
J Sauder, B Sievers - Advances in Neural Information …, 2019 - proceedings.neurips.cc
Point clouds provide a flexible and natural representation usable in countless applications
such as robotics or self-driving cars. Recently, deep neural networks operating on raw point …
such as robotics or self-driving cars. Recently, deep neural networks operating on raw point …
3D2SeqViews: Aggregating sequential views for 3D global feature learning by CNN with hierarchical attention aggregation
Learning 3D global features by aggregating multiple views is important. Pooling is widely
used to aggregate views in deep learning models. However, pooling disregards a lot of …
used to aggregate views in deep learning models. However, pooling disregards a lot of …
A large-scale annotated mechanical components benchmark for classification and retrieval tasks with deep neural networks
We introduce a large-scale annotated mechanical components benchmark for classification
and retrieval tasks named Mechanical Components Benchmark (MCB): a large-scale …
and retrieval tasks named Mechanical Components Benchmark (MCB): a large-scale …
View inter-prediction gan: Unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions
In this paper, we present a novel unsupervised representation learning approach for 3D
shapes, which is an important research challenge as it avoids the manual effort required for …
shapes, which is an important research challenge as it avoids the manual effort required for …
DeepSphere: a graph-based spherical CNN
M Defferrard, M Milani, F Gusset… - arxiv preprint arxiv …, 2020 - arxiv.org
Designing a convolution for a spherical neural network requires a delicate tradeoff between
efficiency and rotation equivariance. DeepSphere, a method based on a graph …
efficiency and rotation equivariance. DeepSphere, a method based on a graph …
SeqViews2SeqLabels: Learning 3D global features via aggregating sequential views by RNN with attention
Learning 3D global features by aggregating multiple views has been introduced as a
successful strategy for 3D shape analysis. In recent deep learning models with end-to-end …
successful strategy for 3D shape analysis. In recent deep learning models with end-to-end …
Learning view-based graph convolutional network for multi-view 3d shape analysis
View-based approach that recognizes 3D shape through its projected 2D images has
achieved state-of-the-art results for 3D shape recognition. The major challenges are how to …
achieved state-of-the-art results for 3D shape recognition. The major challenges are how to …