Search results with tag "Pointnet"
PCT: Point Cloud Transformer - Tsinghua University
cg.cs.tsinghua.edu.cnPointNet [21] pioneered point cloud learning. Subse-quently, Qi et al. proposed PointNet++ [22], which uses query ball grouping and hierarchical PointNet to capture lo-cal structures. Several subsequent works considered how to define convolution operations on point clouds. One main approach is to convert a point cloud into a regular voxel
PCT: Point Cloud Transformer - arXiv
arxiv.orgPointNet [21] pioneered point cloud learning. Subse-quently, Qi et al. proposed PointNet++ [22], which uses query ball grouping and hierarchical PointNet to capture lo-cal structures. Several subsequent works considered how to define convolution operations on point clouds. One main approach is to convert a point cloud into a regular voxel
VoxelNet: End-to-End Learning for Point Cloud Based 3D ...
openaccess.thecvf.comRecently, Qi et al.[31] proposed PointNet, an end-to-end deep neural network that learns point-wise features di-rectly from point clouds. This approach demonstrated im-pressive results on 3D object recognition, 3D object part segmentation, and point-wise semantic segmentation tasks. In [32], an improved version of PointNet was introduced
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object ...
openaccess.thecvf.comconvolution or PointNet-based networks as the backbone. Generally, the 3D voxel sparse CNNs are more efficient [37, 28] and are able to generate high-quality 3D proposals, while the PointNet-based methods can capture more accu-rate contextual information with flexible receptive fields. Our PV-RCNN deeply integrates the advantages of two
Point Transformer
openaccess.thecvf.comPointNet [25] utilizes permutation-invariant operators such as pointwise MLPs and pooling layers to aggregate features across a set. PointNet++ [27] applies these ideas within a hierarchical spatial structure to increase sensitivity to local geometric layout. Such models can benefit from efficient sampling of the point set, and a variety of ...
Point-GNN: Graph Neural Network for 3D Object Detection in ...
openaccess.thecvf.comPoint cloud in sets. Deep learning techniques on sets such as PointNet [3] and DeepSet[22] show neural networks can extract features from an unordered set of points directly. In suchamethod, eachpointisprocessedbyamulti-layerper-ceptron (MLP) to obtain a point feature vector. Those fea-tures are aggregated by an average or max pooling function
SOMA: Solving Optical Marker-Based MoCap Automatically
download.is.tue.mpg.dePointNet methods [7,36] also process the 3D point cloud directly, while learning local features with permutation-invariant pooling operators. Further non-local networks [54] and self-attention-based [52] models can attend glob-ally while learning to focus locally on specific regions of the input. This simple formulation enables learning robust
PointNet++: Deep Hierarchical Feature Learning on Point ...
arxiv.orgPointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space Charles R. Qi Li Yi Hao Su Leonidas J. Guibas Stanford University Abstract Few prior works study deep learning on point sets. PointNet [20] is a pioneer in this direction. However, by design PointNet does not capture local structures induced by
PointNet: Deep Learning on Point Sets for 3D ...
openaccess.thecvf.comPointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Charles R. Qi* Hao Su* Kaichun Mo Leonidas J. Guibas Stanford University Abstract Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers
PointNet++: Deep Hierarchical Feature Learning on Point …
proceedings.neurips.cc3.2 Hierarchical Point Set Feature Learning While PointNet uses a single max pooling operation to aggregate the whole point set, our new architecture builds a hierarchical grouping of points and progressively abstract larger and larger local regions along the hierarchy.
PointNet: Deep Learning on Point Sets ... - CVF Open Access
openaccess.thecvf.comPointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Charles R. Qi* Hao Su* Kaichun Mo Leonidas J. Guibas Stanford University