SOTAVerified

4D Panoptic Segmentation

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

Papers

Showing 19 of 9 papers

TitleStatusHype
4D Panoptic Scene Graph GenerationCode3
LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting NetworkCode2
4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and AggregationCode1
Mask4Former: Mask Transformer for 4D Panoptic SegmentationCode1
4D Panoptic LiDAR SegmentationCode1
Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR SequencesCode1
Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR ScansCode0
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction0
4D-Former: Multimodal 4D Panoptic Segmentation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Mask4FormerLSTQ68.4Unverified
2Eq-4D-StOPLSTQ67.8Unverified
3Mask4DLSTQ64.3Unverified
44D-StOPLSTQ63.9Unverified
5CIALSTQ63.1Unverified
64D-DS-NetLSTQ62.3Unverified
74D-PLSLSTQ56.9Unverified