3D Semantic Segmentation On Kitti 360

Metrics

Model size

miou Val

Results

Performance results of various models on this benchmark

			Paper Title
SuperCluster	790K	62.1	Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering
Superpoint Transformer	777K	63.5	Efficient 3D Semantic Segmentation with Superpoint Transformer
DeepViewAgg	41.2M	57.8	Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation
CLOUDSPAM	37.9M	63.6	CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo
DA-supervised	37.9M	64.1	CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo
MinkowskiNet	37.9M	54.2	Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation
PointNet++	3.0M	-	PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
PointNet	N/A	-	PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

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3D Semantic Segmentation On Kitti 360 | SOTA | HyperAI