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Found 5 papers, 0 papers with code
GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network
GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects in contrast to conventional two-stage panoptic systems, where a detection network is incorporated for capturing instance information.
Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions
Autonomous driving vehicles and robotic systems rely on accurate perception of their surroundings.
(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network
Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority.
Ranked #3 on
3D Semantic Segmentation
on nuScenes
TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module
Semantic segmentation of point clouds is a key component of scene understanding for robotics and autonomous driving.
Ranked #15 on
3D Semantic Segmentation
on SemanticKITTI
Adaptive Hierarchical Down-Sampling for Point Cloud Classification
While several convolution-like operators have recently been proposed for extracting features out of point clouds, down-sampling an unordered point cloud in a deep neural network has not been rigorously studied.
