OccMamba: Semantic Occupancy Prediction with State Space Models

• URL: http://arxiv.org/abs/2408.09859v1
• Date: Mon, 19 Aug 2024 10:07:00 GMT
• Title: OccMamba: Semantic Occupancy Prediction with State Space Models
• Authors: Heng Li, Yuenan Hou, Xiaohan Xing, Xiao Sun, Yanyong Zhang,
• Abstract summary: We present the first Mamba-based network for semantic occupancy prediction, termed OccMamba. We present a simple yet effective 3D-to-1D reordering operation, i.e., height-prioritized 2D Hilbert expansion. OccMamba achieves state-of-the-art performance on three prevalent occupancy prediction benchmarks.
• Score: 16.646162677831985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training deep learning models for semantic occupancy prediction is challenging due to factors such as a large number of occupancy cells, severe occlusion, limited visual cues, complicated driving scenarios, etc. Recent methods often adopt transformer-based architectures given their strong capability in learning input-conditioned weights and long-range relationships. However, transformer-based networks are notorious for their quadratic computation complexity, seriously undermining their efficacy and deployment in semantic occupancy prediction. Inspired by the global modeling and linear computation complexity of the Mamba architecture, we present the first Mamba-based network for semantic occupancy prediction, termed OccMamba. However, directly applying the Mamba architecture to the occupancy prediction task yields unsatisfactory performance due to the inherent domain gap between the linguistic and 3D domains. To relieve this problem, we present a simple yet effective 3D-to-1D reordering operation, i.e., height-prioritized 2D Hilbert expansion. It can maximally retain the spatial structure of point clouds as well as facilitate the processing of Mamba blocks. Our OccMamba achieves state-of-the-art performance on three prevalent occupancy prediction benchmarks, including OpenOccupancy, SemanticKITTI and SemanticPOSS. Notably, on OpenOccupancy, our OccMamba outperforms the previous state-of-the-art Co-Occ by 3.1% IoU and 3.2% mIoU, respectively. Codes will be released upon publication.

Related papers

• UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection [64.65405058535262]
  Recent advances in LiDAR 3D detection have demonstrated the effectiveness of Transformer-based frameworks in capturing the global dependencies from point cloud spaces. 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. 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) 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)
• TransMamba: Fast Universal Architecture Adaption from Transformers to Mamba [88.31117598044725]
  We explore cross-architecture training to transfer the ready knowledge in existing Transformer models to alternative architecture Mamba, termed TransMamba. Our approach employs a two-stage strategy to expedite training new Mamba models, ensuring effectiveness in across uni-modal and cross-modal tasks. For cross-modal learning, we propose a cross-Mamba module that integrates language awareness into Mamba's visual features, enhancing the cross-modal interaction capabilities of Mamba architecture.
  arXiv  Detail & Related papers  (2025-02-21T01:22:01Z)
• Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement [54.427965535613886]
  Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision. In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks.
  arXiv  Detail & Related papers  (2024-12-21T13:43:51Z)
• MobileMamba: Lightweight Multi-Receptive Visual Mamba Network [51.33486891724516]
  Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. We propose the MobileMamba framework, which balances efficiency and performance. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-24T18:01:05Z)
• Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning [54.19222454702032]
  Continual Learning aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowledge. State Space Models (SSMs) have achieved notable success in computer vision. We introduce Mamba-CL, a framework that continuously fine-tunes the core SSMs of the large-scale Mamba foundation model.
  arXiv  Detail & Related papers  (2024-11-23T06:36:16Z)
• ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow Prediction [89.89610257714006]
  Existing methods prioritize higher accuracy to cater to the demands of these tasks. We introduce a series of targeted improvements for 3D semantic occupancy prediction and flow estimation. Our purelytemporalal architecture framework, named ALOcc, achieves an optimal tradeoff between speed and accuracy.
  arXiv  Detail & Related papers  (2024-11-12T11:32:56Z)
• Exploring contextual modeling with linear complexity for point cloud segmentation [43.36716250540622]
  We identify the key components of an effective and efficient point cloud segmentation architecture. We show that Mamba features linear computational complexity, offering superior data and inference efficiency compared to Transformers. We further enhance the standard Mamba specifically for point cloud segmentation by identifying its two key shortcomings.
  arXiv  Detail & Related papers  (2024-10-28T16:56:30Z)
• MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image Segmentation [6.673169053236727]
  We propose MambaClinix, a novel U-shaped architecture for medical image segmentation. MambaClinix integrates a hierarchical gated convolutional network with Mamba in an adaptive stage-wise framework. Our results show that MambaClinix achieves high segmentation accuracy while maintaining low model complexity.
  arXiv  Detail & Related papers  (2024-09-19T07:51:14Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• MambaOcc: Visual State Space Model for BEV-based Occupancy Prediction with Local Adaptive Reordering [31.239405105904574]
  We propose a Mamba-based occupancy prediction method (MambaOcc) adopting BEV features to ease the burden of 3D scenario representation. Experiments on the Occ3D-nuScenes dataset demonstrate that MambaOcc achieves state-of-the-art performance in terms of both accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-08-21T09:29:45Z)
• DiM-Gesture: Co-Speech Gesture Generation with Adaptive Layer Normalization Mamba-2 framework [2.187990941788468]
  generative model crafted to create highly personalized 3D full-body gestures solely from raw speech audio. Model integrates a Mamba-based fuzzy feature extractor with a non-autoregressive Adaptive Layer Normalization (AdaLN) Mamba-2 diffusion architecture.
  arXiv  Detail & Related papers  (2024-08-01T08:22:47Z)
• Mamba YOLO: A Simple Baseline for Object Detection with State Space Model [10.44725284994877]
  YOLO series has set a new benchmark for real-time object detectors. Transformer-based structures have emerged as the most powerful solution. However, the quadratic complexity of the self-attentive mechanism increases the computational burden. We introduce a simple yet effective baseline approach called Mamba YOLO.
  arXiv  Detail & Related papers  (2024-06-09T15:56:19Z)
• MambaDepth: Enhancing Long-range Dependency for Self-Supervised Fine-Structured Monocular Depth Estimation [0.0]
  MambaDepth is a versatile network tailored for self-supervised depth estimation. MambaDepth combines the U-Net's effectiveness in self-supervised depth estimation with the advanced capabilities of Mamba. MambaDepth proves its superior generalization capacities on other datasets such as Make3D and Cityscapes.
  arXiv  Detail & Related papers  (2024-06-06T22:08:48Z)
• MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs [1.7648680700685022]
  Underwater Image Enhancement (UIE) techniques aim to address the problem of underwater image degradation due to light absorption and scattering. Recent years, both Convolution Neural Network (CNN)-based and Transformer-based methods have been widely explored. MambaUIE is able to efficiently synthesize global and local information and maintains a very small number of parameters with high accuracy.
  arXiv  Detail & Related papers  (2024-04-22T05:12:11Z)
• ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model [18.063680125378347]
  Mamba architecture has shown remarkable performance in a series of natural language processing tasks. We tailor the corresponding frameworks, called MambaBCD, MambaSCD, and MambaBDA, for binary change detection, semantic change detection, and building damage assessment. All three frameworks adopt the cutting-edge Visual Mamba architecture as the encoder, which allows full learning of global spatial contextual information from the input images.
  arXiv  Detail & Related papers  (2024-04-04T13:06:25Z)
• MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection [72.46396769642787]
  We develop a nested structure, Mamba-in-Mamba (MiM-ISTD), for efficient infrared small target detection. MiM-ISTD is $8 times$ faster than the SOTA method and reduces GPU memory usage by 62.2$%$ when testing on $2048 times 2048$ images.
  arXiv  Detail & Related papers  (2024-03-04T15:57:29Z)
• PointMamba: A Simple State Space Model for Point Cloud Analysis [65.59944745840866]
  We propose PointMamba, transferring the success of Mamba, a recent representative state space model (SSM), from NLP to point cloud analysis tasks. Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs.
  arXiv  Detail & Related papers  (2024-02-16T14:56:13Z)
• OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception [73.05425657479704]
  We propose OpenOccupancy, which is the first surrounding semantic occupancy perception benchmark. We extend the large-scale nuScenes dataset with dense semantic occupancy annotations. Considering the complexity of surrounding occupancy perception, we propose the Cascade Occupancy Network (CONet) to refine the coarse prediction.
  arXiv  Detail & Related papers  (2023-03-07T15:43:39Z)

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.