ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation

• URL: http://arxiv.org/abs/2606.01549v1
• Date: Mon, 01 Jun 2026 01:49:09 GMT
• Title: ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation
• Abstract summary: ForestMamba is a structure-aware method that incorporates forest-specific priors into feature encoding, query generation, and query refinement.<n>Experiments across seven forest regions demonstrate that ForestMamba consistently outperforms existing baselines in both segmentation tasks.
• Score: 9.432335594963956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-based semantic and instance segmentation of terrestrial and drone LiDAR point clouds is emerging as a transformative approach for converting the complex 3D structure of forests into actionable information for forest monitoring and biodiversity assessment. However, forest LiDAR scenes remain highly challenging due to their large data volumes, irregular sampling density, overlapping and complex canopy structure, and geographic variability. Existing methods based on sparse convolutions or Transformers achieve promising results, but suffer from two key limitations: Quadratic complexity of attention scales poorly to large forest scenes, and Generic context modeling does not exploit forest structural priors, limiting tree separation in complex regions. To address these challenges, we propose ForestMamba, a structure-aware method that incorporates forest-specific priors into feature encoding, query generation, and query refinement, while replacing quadratic attention with linear-time state-space modeling. First, we introduce a sparse encoder with vertical-priority slab serialization that organizes sparse voxels into vertically coherent sequences for efficient long-range context modeling. Second, we propose a geometry-guided query initialization strategy based on an on-the-fly multi-scale Canopy Height Model (CHM), where canopy maxima provide ecologically meaningful query seeds, supplemented by Farthest Point Sampling (FPS) to cover understory trees. Third, we design a Mamba-based query decoder that combines local kNN voxel aggregation with a spatial dual-path Mamba for query refinement with linear computational complexity. Extensive experiments across seven forest regions demonstrate that ForestMamba consistently outperforms existing baselines in both segmentation tasks, while achieving 3 times faster inference and 2.3 times lower GPU memory than Transformer-based methods.

Related papers

• MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection [108.36437360254605]
  This paper proposes MambaADv2, a framework tailored for multi-class unsupervised anomaly detection.<n>MambaADv2 comprises a pre-trained encoder and a Mamba-inspired decoder, equipped with Duality-enhanced State Space (DSS) modules across multiple scales.<n>The structure of the Hybrid State Space (HSS) block is tailored by following the SSD-based Mamba lineage and incorporating Mamba3-style position-aware state-space modeling.
  arXiv  Detail & Related papers  (2026-06-22T10:14:06Z)
• MambaPanoptic: A Vision Mamba-based Structured State Space Framework for Panoptic Segmentation [44.88330743454271]
  MambaPanoptic is a fully Mamba-based panoptic segmentation framework.<n>We introduce MambaFPN, a top-down feature pyramid that leverages Mamba blocks to generate globally coherent, multi-scale feature representations.<n>Experiments on the Cityscapes and panoptic segmentation benchmarks demonstrate that MambaPanoptic consistently outperforms PanopticDeepLab and PanopticFCN.
  arXiv  Detail & Related papers  (2026-05-12T18:30:49Z)
• LDRFusion: A LiDAR-Dominant multimodal refinement framework for 3D object detection [5.820124526272312]
  Existing LiDAR-Camera fusion methods have achieved strong results in 3D object detection.<n>We propose LDRFusion, a novel Lidar-dominant two-stage refinement framework for multi-sensor fusion.<n>Our framework consistently achieves strong performance across multiple categories and difficulty levels.
  arXiv  Detail & Related papers  (2025-07-22T04:35:52Z)
• ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds [0.06282171844772422]
  We present ForestFormer3D, a new unified and end-to-end framework for precise individual tree and semantic segmentation.<n>ForestFormer3D incorporates ISA-guided query point selection, a score-based block merging strategy during inference, and a one-to-many association mechanism for effective training.<n>Our model achieves state-of-the-art performance for individual tree segmentation on the newly introduced FOR-instanceV2 dataset.
  arXiv  Detail & Related papers  (2025-06-20T13:39:27Z)
• PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter [54.33433051500349]
  We propose Point Mamba Adapter (PMA), which constructs an ordered feature sequence from all layers of the pre-trained model.<n>We also propose a geometry-constrained gate prompt generator (G2PG) shared across different layers.
  arXiv  Detail & Related papers  (2025-05-27T09:27:16Z)
• UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection [53.785766442201094]
  Recent advances in LiDAR 3D detection have demonstrated the effectiveness of Transformer-based frameworks in capturing the global dependencies from point cloud spaces.<n>Due to the considerable number of 3D voxels and quadratic complexity of Transformers, multiple sequences are grouped before feeding to Transformers, leading to a limited receptive field.<n>Inspired by the impressive performance of State Space Models (SSM) achieved in the field of 2D vision tasks, we propose a novel Unified Mamba (UniMamba)<n>Specifically, a UniMamba block is designed which mainly consists of locality modeling, Z-order serialization and local-global sequential aggregator.
  arXiv  Detail & Related papers  (2025-03-15T06:22:31Z)
• NIMBA: Towards Robust and Principled Processing of Point Clouds With SSMs [9.978766637766373]
  We introduce a method to convert point clouds into 1D sequences that maintain 3D spatial structure with no need for data replication. Our method does not require positional embeddings and allows for shorter sequence lengths while still achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-31T18:58:40Z)
• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection [59.34834815090167]
  Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D object detection. We present a Voxel SSM, which employs a group-free strategy to serialize the whole space of voxels into a single sequence.
  arXiv  Detail & Related papers  (2024-06-15T17:45:07Z)
• Point Cloud Mamba: Point Cloud Learning via State Space Model [73.7454734756626]
  We show that Mamba-based point cloud methods can outperform previous methods based on transformer or multi-layer perceptrons (MLPs) In particular, we demonstrate that Mamba-based point cloud methods can outperform previous methods based on transformer or multi-layer perceptrons (MLPs) Point Cloud Mamba surpasses the state-of-the-art (SOTA) point-based method PointNeXt and achieves new SOTA performance on the ScanNN, ModelNet40, ShapeNetPart, and S3DIS datasets.
  arXiv  Detail & Related papers  (2024-03-01T18:59:03Z)
• Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans [4.129847064263057]
  We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m2 with dense 3D annotation. We propose a 3D deep network architecture predicting for the first time both 3D point-wise labels and high-resolution occupancy meshes simultaneously.
  arXiv  Detail & Related papers  (2022-04-25T12:47:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.