Attention-based point cloud edge sampling
Point cloud sampling is a less explored research topic for this data representation. The most
commonly used sampling methods are still classical random sampling and farthest point …
commonly used sampling methods are still classical random sampling and farthest point …
Lightn: Light-weight transformer network for performance-overhead tradeoff in point cloud downsampling
Downsampling is a crucial task for processing large scale and/or dense point clouds with
limited resources. Owing to the development of deep learning, approaches of task-oriented …
limited resources. Owing to the development of deep learning, approaches of task-oriented …
Hierarchical edge aware learning for 3d point cloud
L Li - Computer Graphics International Conference, 2023 - Springer
This paper proposes an innovative approach to H ierarchical E dge A ware 3D Point Cloud
Learning (HEA-Net) that seeks to address the challenges of noise in point cloud data, and …
Learning (HEA-Net) that seeks to address the challenges of noise in point cloud data, and …
APSNet: Attention based point cloud sampling
Processing large point clouds is a challenging task. Therefore, the data is often
downsampled to a smaller size such that it can be stored, transmitted and processed more …
downsampled to a smaller size such that it can be stored, transmitted and processed more …
Edge aware learning for 3d point cloud
L Li - arxiv preprint arxiv:2309.13472, 2023 - arxiv.org
This paper proposes an innovative approach to Hierarchical Edge Aware 3D Point Cloud
Learning (HEA-Net) that seeks to address the challenges of noise in point cloud data, and …
Learning (HEA-Net) that seeks to address the challenges of noise in point cloud data, and …
PIF-Net: A Deep Point-Image Fusion Network for Multi-Modality Semantic Segmentation of Very High Resolution Imagery and Aerial Point Cloud
Semantic segmentation is of great significance in many applications. However, automating
such a task on single-modality data is challenging in the field of remote sensing due to …
such a task on single-modality data is challenging in the field of remote sensing due to …
Compressed point cloud classification with point-based edge sampling
Abstract 3D point cloud data, as an immersive detailed data source, has been increasingly
used in numerous applications. To deal with the computational and storage challenges of …
used in numerous applications. To deal with the computational and storage challenges of …
CAS-Net: Cascade Attention-Based Sampling Neural Network for Point Cloud Simplification
Point cloud sampling can reduce storage requirements and computation costs for various
vision tasks. Traditional sampling methods, such as farthest point sampling, are not geared …
vision tasks. Traditional sampling methods, such as farthest point sampling, are not geared …
AS-PD: An Arbitrary-Size Downsampling Framework for Point Clouds
Point cloud downsampling is a crucial pre-processing operation to downsample points in
order to unify data size and reduce computational cost, to name a few. Recent research on …
order to unify data size and reduce computational cost, to name a few. Recent research on …
Learnable Point Cloud Sampling Considering Seed Point for Neural Surface Reconstruction
K Matsuzaki, K Nonaka - IEEE Access, 2024 - ieeexplore.ieee.org
Reconstruction of surfaces from point clouds is essential in numerous practical applications.
An approach in which neural fields are trained as surface representations from point clouds …
An approach in which neural fields are trained as surface representations from point clouds …