Point Cloud Generation
44 papers with code • 4 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Point Cloud Generation
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ShapeNet
JetClass
Most implemented papers
Progressive Point Cloud Deconvolution Generation Network
Starting from the low-resolution point clouds, with the bilateral interpolation and max-pooling operations, the deconvolution network can progressively output high-resolution local and global feature maps.
Discrete Point Flow Networks for Efficient Point Cloud Generation
Regenerator/dpf-nets
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ECCV 2020
Generative models have proven effective at modeling 3D shapes and their statistical variations.
Learning Gradient Fields for Shape Generation
RuojinCai/ShapeGF
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ECCV 2020
Point cloud generation thus amounts to moving randomly sampled points to high-density areas.
Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projections Matching
chenchao15/2d_projection_matching
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ICCV 2021
Our method pushes the neural network to generate a 3D point cloud whose 2D projections match the irregular point supervision from different view angles.
Learning to Drop Points for LiDAR Scan Synthesis
As in the related studies, we process LiDAR data as a compact yet lossless representation, a cylindrical depth map.
Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching
chenchao15/2d_projection_matching
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Our method pushes the neural network to generate a 3D point cloud whose 2D projections match the irregular point supervision from different view angles.
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr that adopts a transformer encoder-decoder architecture for point cloud completion.
Flow Plugin Network for conditional generation
pfilo8/flow-plugin-network-for-conditional-generation
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Generative models have gained many researchers' attention in the last years resulting in models such as StyleGAN for human face generation or PointFlow for the 3D point cloud generation.
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
zhaoyanglyu/point_diffusion_refinement
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ICLR 2022
The RFNet refines the coarse output of the CGNet and further improves quality of the completed point cloud.
Attention-based Transformation from Latent Features to Point Clouds
The points generated by AXform do not have the strong 2-manifold constraint, which improves the generation of non-smooth surfaces.
