LIDAR Semantic Segmentation
53 papers with code • 4 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in LIDAR Semantic Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find LIDAR Semantic Segmentation models and implementationsDatasets
nuScenes
SemanticKITTI
ScribbleKITTI
Paris-Lille-3D
A Curb Dataset
nuScenes (Cross-City UDA)
ULS labeled data
Latest papers with no code
Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation
Safety-critical applications like autonomous driving call for robust 3D environment perception algorithms which can withstand highly diverse and ambiguous surroundings.
UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather
We devote UniMix to two main setups: 1) unsupervised domain adaption, adapting the model from the clear weather source domain to the adverse weather target domain; 2) domain generalization, learning a model that generalizes well to unseen scenes in adverse weather.
LVIC: Multi-modality segmentation by Lifting Visual Info as Cue
In our experiments, we find that this projection error is the devil in point painting.
Domain Generalization in LiDAR Semantic Segmentation Leveraged by Density Discriminative Feature Embedding
Understanding this, we view each LiDAR's point cloud at various distances as having distinct density distributions, which can be consistent across different LiDAR models.
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation
As 3D perception problems grow in popularity and the need for large-scale labeled datasets for LiDAR semantic segmentation increase, new methods arise that aim to reduce the necessity for dense annotations by employing weakly-supervised training.
COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous driving
LiDAR semantic segmentation for autonomous driving has been a growing field of interest in the past few years.
MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory
To address this challenge, we propose a novel framework for semantic segmentation of a temporal sequence of LiDAR point clouds that utilizes a memory network to store, update and retrieve past information.
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation
In the realm of LiDAR semantic segmentation, the challenges stem from the sheer volume of point clouds, rendering annotation labor-intensive and cost-prohibitive.
ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception
LiDAR is an essential sensor for autonomous driving by collecting precise geometric information regarding a scene.
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentation
By sampling object clusters according to their size, we can thus create a size-balanced dataset that is also more class-balanced.
