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Found 7 papers, 1 papers with code
GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Clouds
LiDAR-based 3D detection has made great progress in recent years.
Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning
We consider the setting where we have a fully-labeled data set from source domain and a target domain with a few labeled and many unlabeled examples.
SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering
Thus, we propose to use a novel centroid-aware repel loss as an additional term to effectively supervise the network to differentiate each object cluster with its neighbours.
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.
S3Net: 3D LiDAR Sparse Semantic Segmentation Network
In literature, several approaches are introduced to attempt LiDAR semantic segmentation task, such as projection-based (range-view or birds-eye-view), and voxel-based approaches.
S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds
With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike.
Ranked #2 on
3D Semantic Scene Completion
on SemanticKITTI
