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Found 6 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
Point Cloud Learning with Transformer
Remarkable performance from Transformer networks in Natural Language Processing promote the development of these models in dealing with computer vision tasks such as image recognition and segmentation.
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.
Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era
Given this new era of rapid evolution, this article provides a comprehensive survey of the recent developments in this field.
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.
