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Found 5 papers, 1 papers with code
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds
To reduce the memory and computational cost, existing point-based pipelines usually adopt task-agnostic random sampling or farthest point sampling to progressively downsample input point clouds, despite the fact that not all points are equally important to the task of object detection.
Adaptive Channel Encoding for Point Cloud Analysis
Attention mechanism plays a more and more important role in point cloud analysis and channel attention is one of the hotspots.
Adaptive Channel Encoding Transformer for Point Cloud Analysis
Transformer plays an increasingly important role in various computer vision areas and remarkable achievements have also been made in point cloud analysis.
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis
To handle this prob-lem, a feature representation learning method, named Dual-Neighborhood Deep Fusion Network (DNDFN), is proposed to serve as an improved point cloud encoder for the task of non-idealized point cloud classification.
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition
The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets.
