:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Lin, Xuanyu, Zeng, Xiaona, Zheng, Xianwei, Li, Xutao
Formato:	Preprint
Publicado:	2025
Materias:	Computer Vision and Pattern Recognition
Acceso en línea:	https://arxiv.org/abs/2507.17296
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Efficient Spiking Point Mamba for Point Cloud Analysis
por: Wu, Peixi, et al.
Publicado: (2025)

DAPointMamba: Domain Adaptive Point Mamba for Point Cloud Completion
por: Li, Yinghui, et al.
Publicado: (2025)

ZigzagPointMamba: Spatial-Semantic Mamba for Point Cloud Understanding
por: Diao, Linshuang, et al.
Publicado: (2025)

Point Cloud Mamba: Point Cloud Learning via State Space Model
por: Zhang, Tao, et al.
Publicado: (2024)

Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model
por: Wang, Tao, et al.
Publicado: (2024)

MambaPlace:Text-to-Point-Cloud Cross-Modal Place Recognition with Attention Mamba Mechanisms
por: Shang, Tianyi, et al.
Publicado: (2024)

PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter
por: Zha, Yaohua, et al.
Publicado: (2025)

Diffusion Masked Pretraining for Dynamic Point Cloud
por: Zhang, Zhuoyue, et al.
Publicado: (2026)

PointMamba: A Simple State Space Model for Point Cloud Analysis
por: Liang, Dingkang, et al.
Publicado: (2024)

Spectral Informed Mamba for Robust Point Cloud Processing
por: Bahri, Ali, et al.
Publicado: (2025)

CloudMamba: Grouped Selective State Spaces for Point Cloud Analysis
por: Qu, Kanglin, et al.
Publicado: (2025)

TFDM: Time-Variant Frequency-Based Point Cloud Diffusion with Mamba
por: Liu, Jiaxu, et al.
Publicado: (2025)

FlowMamba: Learning Point Cloud Scene Flow with Global Motion Propagation
por: Lin, Min, et al.
Publicado: (2024)

Mantis: Mamba-native Tuning is Efficient for 3D Point Cloud Foundation Models
por: Guo, Zihao, et al.
Publicado: (2026)

PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecture
por: Zheng, Qiang, et al.
Publicado: (2024)

Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy
por: Liu, Jiuming, et al.
Publicado: (2024)

Rethinking Attention Module Design for Point Cloud Analysis
por: Wu, Chengzhi, et al.
Publicado: (2024)

Pix4Point: Image Pretrained Standard Transformers for 3D Point Cloud Understanding
por: Qian, Guocheng, et al.
Publicado: (2022)

FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation
por: Zhou, Chenliang, et al.
Publicado: (2023)

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning
por: Wang, Chuxin, et al.
Publicado: (2025)

ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation
por: Nguyen, Trung Thanh, et al.
Publicado: (2026)

Efficient Implicit Neural Compression of Point Clouds via Learnable Activation in Latent Space
por: Zhang, Yichi, et al.
Publicado: (2025)

PointViG: A Lightweight GNN-based Model for Efficient Point Cloud Analysis
por: Zheng, Qiang, et al.
Publicado: (2024)

RaLD: Generating High-Resolution 3D Radar Point Clouds with Latent Diffusion
por: Zhang, Ruijie, et al.
Publicado: (2025)

PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis
por: Wang, Zicheng, et al.
Publicado: (2024)

PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learning
por: He, Qingdong, et al.
Publicado: (2024)

Fully-Geometric Cross-Attention for Point Cloud Registration
por: Wang, Weijie, et al.
Publicado: (2025)

PointGL: A Simple Global-Local Framework for Efficient Point Cloud Analysis
por: Li, Jianan, et al.
Publicado: (2024)

DiffPCN: Latent Diffusion Model Based on Multi-view Depth Images for Point Cloud Completion
por: Li, Zijun, et al.
Publicado: (2025)

FLaTEC: Frequency-Disentangled Latent Triplanes for Efficient Compression of LiDAR Point Clouds
por: Zhang, Xiaoge, et al.
Publicado: (2025)

Efficient Point Transformer with Dynamic Token Aggregating for LiDAR Point Cloud Processing
por: Lu, Dening, et al.
Publicado: (2024)

Point-RTD: Replaced Token Denoising for Pretraining Transformer Models on Point Clouds
por: Stone, Gunner, et al.
Publicado: (2025)

Mamba Learns in Context: Structure-Aware Domain Generalization for Multi-Task Point Cloud Understanding
por: Jiang, Jincen, et al.
Publicado: (2026)

MT-PCR: Hybrid Mamba-Transformer Network with Spatial Serialization for Point Cloud Registration
por: Liu, Bingxi, et al.
Publicado: (2025)

HydraMamba: Multi-Head State Space Model for Global Point Cloud Learning
por: Qu, Kanglin, et al.
Publicado: (2025)

SRMamba: Mamba for Super-Resolution of LiDAR Point Clouds
por: Chen, Chuang, et al.
Publicado: (2025)

CLIP-based Point Cloud Classification via Point Cloud to Image Translation
por: Ghose, Shuvozit, et al.
Publicado: (2024)

ProxyTransformation: Preshaping Point Cloud Manifold With Proxy Attention For 3D Visual Grounding
por: Peng, Qihang, et al.
Publicado: (2025)

Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models
por: Romanelis, Ioannis, et al.
Publicado: (2024)

3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining
por: Yan, Siming, et al.
Publicado: (2023)