Transcription of PointNet: Deep Learning on Point Sets for 3D Classification ...
{{id}} {{{paragraph}}}
Example: biology
PointNet: Deep Learning on Point Sets for 3D Classification ...
PointNet: Deep Learning on Point Sets for 3D Classification and SegmentationCharles R. Qi*Hao Su*Kaichun MoLeonidas J. GuibasStanford UniversityAbstractPoint cloud is an important type of geometric datastructure. Due to its irregular format, most researcherstransform such data to regular 3D voxel grids or collectionsof , however, renders data unnecessarilyvoluminous and causes issues. In this paper, we design anovel type of neural network that directly consumes pointclouds, which well respects the permutation invariance ofpoints in the input. Our network, named PointNet, pro-vides a unified architecture for applications ranging fromobject classification, part segmentation, to scene semanticparsing.
shape classification and retrieval tasks [21]. However, it’s nontrivial to extend them to scene understanding or other 3D tasks such as point classification and shape completion. Spectral CNNs: Some latest works [4,16] use spectral CNNs on meshes. However, these methods are currently constrained on manifold meshes such as organic objects
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: