4D Panoptic Segmentation
4 papers with code • 1 benchmarks • 1 datasets
4D Panoptic Segmentation is a computer vision task that extends video panoptic segmentation to point cloud sequences. That is, given a point cloud sequence, the goal is to predict the semantic class of each point while consistently tracking object instances. Here, the points belonging to the same object instance should be assigned the same instance ID throughout the point cloud sequence. LSTQ metric is used to evaluate the performance of this task. Video credit: Mask4Former
Benchmarks
These leaderboards are used to track progress in 4D Panoptic Segmentation
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SemanticKITTI
Most implemented papers
4D Panoptic LiDAR Segmentation
mehmetaygun/4d-pls
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CVPR 2021
In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points.
LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting Network
hongfz16/DS-Net
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In this work, we address the task of LiDAR-based panoptic segmentation, which aims to parse both objects and scenes in a unified manner.
4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation
Our voting-based tracklet generation method followed by geometric feature-based aggregation generates significantly improved panoptic LiDAR segmentation quality when compared to modeling the entire 4D volume using Gaussian probability distributions.
Mask4Former: Mask Transformer for 4D Panoptic Segmentation
With this intention, we propose Mask4Former for the challenging task of 4D panoptic segmentation of LiDAR point clouds.
