Point cloud classification: research map
731 accepted papers on Point cloud classification in Point clouds, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 3 clusters and 10 approaches. The busiest year so far is 2025.
Within Point clouds, its share held steady from 50.9% in 2023–24 to 50.9% in 2025–26 (232 → 259 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
adversarial · convolution · invariant · 324 papers
Approaches in this cluster:
- Local feature aggregation networks (158 papers)
Design point-set convolutions and feature extractors for point cloud recognition. - Equivariant point cloud networks (84 papers)
Build rotation-equivariant and invariant architectures for point clouds. - Efficient point sampling architectures (45 papers)
Improve point cloud backbones with sampling, scaling and spiking networks. - Adversarial point cloud attacks (37 papers)
Craft and defend against imperceptible adversarial perturbations on point clouds.
Most cited and most cited since 2024:
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (CVPR 2017 · 9,885 citations)
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (NeurIPS 2017 · 2,147 citations)
- Point Transformer V3: Simpler Faster Stronger (CVPR 2024 · 600 citations)
- HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction (CVPR 2024 · 29 citations)
motion · estimation · scene · 205 papers
Approaches in this cluster:
- Scene flow and dynamic point clouds (89 papers)
Estimate motion and process sequences of dynamic point clouds. - Point cloud detection and tracking (56 papers)
Apply point cloud networks to tracking, completion and anomaly detection tasks. - LiDAR point cloud compression and completion (60 papers)
Compress, upsample and complete LiDAR point clouds with learned models.
Most cited and most cited since 2024:
- GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping (CVPR 2020 · 667 citations)
- Point-NeRF: Point-Based Neural Radiance Fields (CVPR 2022 · 561 citations)
- RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception (CVPR 2024 · 58 citations)
- SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction (CVPR 2024 · 53 citations)
language · pre · understanding · 202 papers
Approaches in this cluster:
- Point-language and open-vocabulary 3D (107 papers)
Align 3D point clouds with language for zero-shot and open-vocabulary understanding. - Self-supervised point cloud pretraining (73 papers)
Pretrain point representations via cross-modal, contrastive and diffusion objectives. - Masked point autoencoders (22 papers)
Pretrain point clouds by reconstructing masked patches, often multi-scale or rotation-invariant.
Most cited and most cited since 2024:
- Point-BERT: Pre-Training 3D Point Cloud Transformers With Masked Point Modeling (CVPR 2022 · 780 citations)
- PointCLIP: Point Cloud Understanding by CLIP (CVPR 2022 · 435 citations)
- LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding Reasoning and Planning (CVPR 2024 · 84 citations)
- Point Cloud Mamba: Point Cloud Learning via State Space Model (AAAI 2025 · 82 citations)