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Deep learning for 3d point clouds: A survey
Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …
Deep learning for lidar point clouds in autonomous driving: A review
Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D
LiDAR data has led to rapid development in the field of autonomous driving. However …
LiDAR data has led to rapid development in the field of autonomous driving. However …
2dpass: 2d priors assisted semantic segmentation on lidar point clouds
As camera and LiDAR sensors capture complementary information in autonomous driving,
great efforts have been made to conduct semantic segmentation through multi-modality data …
great efforts have been made to conduct semantic segmentation through multi-modality data …
An end-to-end transformer model for 3d object detection
We propose 3DETR, an end-to-end Transformer based object detection model for 3D point
clouds. Compared to existing detection methods that employ a number of 3D-specific …
clouds. Compared to existing detection methods that employ a number of 3D-specific …
Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds
Abstract We introduce Position Adaptive Convolution (PAConv), a generic convolution
operation for 3D point cloud processing. The key of PAConv is to construct the convolution …
operation for 3D point cloud processing. The key of PAConv is to construct the convolution …
Neural 3d scene reconstruction with the manhattan-world assumption
This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view
images. Many previous works have shown impressive reconstruction results on textured …
images. Many previous works have shown impressive reconstruction results on textured …
Contrastive boundary learning for point cloud segmentation
Point cloud segmentation is fundamental in understanding 3D environments. However,
current 3D point cloud segmentation methods usually perform poorly on scene boundaries …
current 3D point cloud segmentation methods usually perform poorly on scene boundaries …
Oa-cnns: Omni-adaptive sparse cnns for 3d semantic segmentation
The booming of 3D recognition in the 2020s began with the introduction of point cloud
transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models …
transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models …
SCF-Net: Learning spatial contextual features for large-scale point cloud segmentation
How to learn effective features from large-scale point clouds for semantic segmentation has
attracted increasing attention in recent years. Addressing this problem, we propose a …
attracted increasing attention in recent years. Addressing this problem, we propose a …
Semantic segmentation for real point cloud scenes via bilateral augmentation and adaptive fusion
Given the prominence of current 3D sensors, a fine-grained analysis on the basic point
cloud data is worthy of further investigation. Particularly, real point cloud scenes can …
cloud data is worthy of further investigation. Particularly, real point cloud scenes can …