# Attention-based Point Cloud Edge Sampling

arXiv: https://arxiv.org/abs/2302.14673 · HTML: https://mlsanity.com/paper/2302.14673/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 60 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [Relation-Shape Convolutional Neural Network for Point Cloud Analysis](https://mlsanity.com/paper/1904.07601/) (2019)
- [Point-Voxel CNN for Efficient 3D Deep Learning](https://mlsanity.com/paper/1907.03739/) (2019)
- [Understanding Neural Networks Through Deep Visualization](https://mlsanity.com/paper/1506.06579/) (2015)
- [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://mlsanity.com/paper/1704.02901/) (2017)
- [SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters](https://mlsanity.com/paper/1803.11527/) (2018)
- [PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling](https://mlsanity.com/paper/2003.00492/) (2020)
- [PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds](https://mlsanity.com/paper/2103.14635/) (2021)
- [PU-Net: Point Cloud Upsampling Network](https://mlsanity.com/paper/1801.06761/) (2018)
- [3D Object Detection with Pointformer](https://mlsanity.com/paper/2012.11409/) (2020)
- [Tangent Convolutions for Dense Prediction in 3D](https://mlsanity.com/paper/1807.02443/) (2018)
- [Stratified Transformer for 3D Point Cloud Segmentation](https://mlsanity.com/paper/2203.14508/) (2022)
- [SPLATNet: Sparse Lattice Networks for Point Cloud Processing](https://mlsanity.com/paper/1802.08275/) (2018)
- [Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling](https://mlsanity.com/paper/1904.03375/) (2019)
- [Grid-GCN for Fast and Scalable Point Cloud Learning](https://mlsanity.com/paper/1912.02984/) (2019)
- [P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds](https://mlsanity.com/paper/2005.13888/) (2020)
- [Deep Projective 3D Semantic Segmentation](https://mlsanity.com/paper/1705.03428/) (2017)
- [SampleNet: Differentiable Point Cloud Sampling](https://mlsanity.com/paper/1912.03663/) (2019)
- [PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation](https://mlsanity.com/paper/1807.00652/) (2018)

## Built on by (most cited first, 30 of 37)

- [PointMamba: A Simple State Space Model for Point Cloud Analysis](https://mlsanity.com/paper/2402.10739/) (2024)
- [Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders](https://mlsanity.com/paper/2412.17808/) (2024)
- [Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis](https://mlsanity.com/paper/2403.01439/) (2024)
- [PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis](https://mlsanity.com/paper/2405.15463/) (2024)
- [PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learning](https://mlsanity.com/paper/2405.15214/) (2024)
- [Open Panoramic Segmentation](https://mlsanity.com/paper/2407.02685/) (2024)
- [PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning](https://mlsanity.com/paper/2504.16023/) (2025)
- [DV-Matcher: Deformation-based Non-Rigid Point Cloud Matching Guided by Pre-trained Visual Features](https://mlsanity.com/paper/2408.08568/) (2024)
- [Spectral Informed Mamba for Robust Point Cloud Processing](https://mlsanity.com/paper/2503.04953/) (2025)
- [MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing](https://mlsanity.com/paper/2411.16773/) (2024)
- [Efficient Spiking Point Mamba for Point Cloud Analysis](https://mlsanity.com/paper/2504.14371/) (2025)
- [GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation](https://mlsanity.com/paper/2305.15213/) (2023)
- [Edge Aware Learning for 3D Point Cloud](https://mlsanity.com/paper/2309.13472/) (2023)
- [Large Language Models for Knowledge Graph Embedding: A Survey](https://mlsanity.com/paper/2501.07766/) (2025)
- [Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet](https://mlsanity.com/paper/2509.05198/) (2025)
- [CloudMamba: Grouped Selective State Spaces for Point Cloud Analysis](https://mlsanity.com/paper/2511.07823/) (2025)
- [Rethinking Attention Module Design for Point Cloud Analysis](https://mlsanity.com/paper/2407.19294/) (2024)
- [HydraMamba: Multi-Head State Space Model for Global Point Cloud Learning](https://mlsanity.com/paper/2507.19778/) (2025)
- [AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understanding](https://mlsanity.com/paper/2402.17521/) (2024)
- [GSTran: Joint Geometric and Semantic Coherence for Point Cloud Segmentation](https://mlsanity.com/paper/2408.11558/) (2024)
- [Curvature Informed Furthest Point Sampling](https://mlsanity.com/paper/2411.16995/) (2024)
- [SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformity](https://mlsanity.com/paper/2504.19581/) (2025)
- [A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning](https://mlsanity.com/paper/2505.24641/) (2025)
- [Deep Learning For Point Cloud Denoising: A Survey](https://mlsanity.com/paper/2508.11932/) (2025)
- [GFT: Graph Feature Tuning for Efficient Point Cloud Analysis](https://mlsanity.com/paper/2511.10799/) (2025)
- [NPNet: A Non-Parametric Network with Adaptive Gaussian-Fourier Positional Encoding for 3D Classification and Segmentation](https://mlsanity.com/paper/2602.00542/) (2026)
- [SLNet: A Super-Lightweight Geometry-Adaptive Network for 3D Point Cloud Recognition](https://mlsanity.com/paper/2603.07454/) (2026)
- [GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian Manifolds](https://mlsanity.com/paper/2303.15225/) (2023)
- [REPS: Reconstruction-based Point Cloud Sampling](https://mlsanity.com/paper/2403.05047/) (2024)
- [Efficient Point Transformer with Dynamic Token Aggregating for LiDAR Point Cloud Processing](https://mlsanity.com/paper/2405.15827/) (2024)

## Abstract (arXiv)

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 sampling. With the development of neural networks, various methods have been proposed to sample point clouds in a task-based learning manner. However, these methods are mostly generative-based, rather than selecting points directly using mathematical statistics. Inspired by the Canny edge detection algorithm for images and with the help of the attention mechanism, this paper proposes a non-generative Attention-based Point cloud Edge Sampling method (APES), which captures salient points in the point cloud outline. Both qualitative and quantitative experimental results show the superior performance of our sampling method on common benchmark tasks.