LIDAR Semantic Segmentation
12 papers with code • 3 benchmarks • 4 datasets
However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited.
Ranked #2 on 3D Semantic Segmentation on SemanticKITTI
A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space.
Ranked #6 on LIDAR Semantic Segmentation on nuScenes
3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation
Fast and efficient semantic segmentation methods are needed to match the strong computational and temporal restrictions of many of these real-world applications.
Ranked #1 on Real-Time 3D Semantic Segmentation on SemanticKITTI
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling
This work presents FG-Net, a general deep learning framework for large-scale point clouds understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 GPU.
Ranked #1 on 3D Part Segmentation on ShapeNet-Part
In this paper, we propose a new projection-based LiDAR semantic segmentation pipeline that consists of a novel network structure and an efficient post-processing step.