3D Point Cloud Interpolation
3 papers with code • 2 benchmarks • 2 datasets
Point cloud interpolation is a fundamental problem for 3D computer vision. Given a low temporal resolution (frame rate) point cloud sequence, the target of interpolation is to generate a smooth point cloud sequence with high temporal resolution (frame rate).
DHB Dataset
NL-Drive
Most implemented papers
PointINet: Point Cloud Frame Interpolation Network
Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras.
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
zengyiming-eamon/idea-net
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CVPR 2022
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation.
NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation
ispc-lab/neuralpci
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CVPR 2023
In light of these issues, we present NeuralPCI: an end-to-end 4D spatio-temporal Neural field for 3D Point Cloud Interpolation, which implicitly integrates multi-frame information to handle nonlinear large motions for both indoor and outdoor scenarios.
