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Found 5 papers, 1 papers with code
CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
Accurate and fast scene understanding is one of the challenging task for autonomous driving, which requires to take full advantage of LiDAR point clouds for semantic segmentation.
Ranked #9 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation
First, a point-wise feature pyramid module is introduced to hierarchically extract features from different scales or resolutions.
Dual Transformer for Point Cloud Analysis
Following the tremendous success of transformer in natural language processing and image understanding tasks, in this paper, we present a novel point cloud representation learning architecture, named Dual Transformer Network (DTNet), which mainly consists of Dual Point Cloud Transformer (DPCT) module.
Multiple Independent Subspace Clusterings
To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering.
3D Point Cloud Descriptors in Hand-crafted and Deep Learning Age: State-of-the-Art
The introduction of inexpensive 3D data acquisition devices has promisingly facilitated the wide availability and popularity of 3D point cloud, which attracts more attention to the effective extraction of novel 3D point cloud descriptors for accuracy of the efficiency of 3D computer vision tasks in recent years.
