EdgePoint2: Compact Descriptors for Superior Efficiency and Accuracy

• URL: http://arxiv.org/abs/2504.17280v1
• Date: Thu, 24 Apr 2025 06:14:01 GMT
• Title: EdgePoint2: Compact Descriptors for Superior Efficiency and Accuracy
• Authors: Haodi Yao, Fenghua He, Ning Hao, Chen Xie,
• Abstract summary: We present EdgePoint2, a series of lightweight keypoint detection and description neural networks specifically tailored for edge computing applications on embedded system.<n>Our experiments demonstrate that EdgePoint2 consistently achieves state-of-the-art (SOTA) accuracy and efficiency across various challenging scenarios.
• Score: 0.8437187555622164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The field of keypoint extraction, which is essential for vision applications like Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM), has evolved from relying on handcrafted methods to leveraging deep learning techniques. While deep learning approaches have significantly improved performance, they often incur substantial computational costs, limiting their deployment in real-time edge applications. Efforts to create lightweight neural networks have seen some success, yet they often result in trade-offs between efficiency and accuracy. Additionally, the high-dimensional descriptors generated by these networks poses challenges for distributed applications requiring efficient communication and coordination, highlighting the need for compact yet competitively accurate descriptors. In this paper, we present EdgePoint2, a series of lightweight keypoint detection and description neural networks specifically tailored for edge computing applications on embedded system. The network architecture is optimized for efficiency without sacrificing accuracy. To train compact descriptors, we introduce a combination of Orthogonal Procrustes loss and similarity loss, which can serve as a general approach for hypersphere embedding distillation tasks. Additionally, we offer 14 sub-models to satisfy diverse application requirements. Our experiments demonstrate that EdgePoint2 consistently achieves state-of-the-art (SOTA) accuracy and efficiency across various challenging scenarios while employing lower-dimensional descriptors (32/48/64). Beyond its accuracy, EdgePoint2 offers significant advantages in flexibility, robustness, and versatility. Consequently, EdgePoint2 emerges as a highly competitive option for visual tasks, especially in contexts demanding adaptability to diverse computational and communication constraints.

Related papers

• Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment [59.61554561979589]
  Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios.<n>Existing edge detection methods face challenges: difficulty balancing detection precision with lightweight models, limited adaptability, and insufficient real-world validation.<n>We propose the Edge Detection Toolbox (ED-TOOLBOX), which utilizes generalizable plug-and-play components to adapt object detection models for edge environments.
  arXiv  Detail & Related papers  (2024-12-24T07:28:10Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning [9.900317349372383]
  Federated Learning (FL) provides a privacy-preserving framework for training machine learning models on mobile edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy communication workload on these devices. We propose a two-tier HFEL system, where edge devices are connected to edge servers and edge servers are interconnected through peer-to-peer (P2P) edge backhauls. Our goal is to enhance the training efficiency of the HFEL system through strategic resource allocation and topology design.
  arXiv  Detail & Related papers  (2024-09-29T01:48:04Z)
• Optimizing Sensor Network Design for Multiple Coverage [0.9668407688201359]
  We introduce a new objective function for the greedy (next-best-view) algorithm to design efficient and robust sensor networks. We also introduce a Deep Learning model to accelerate the algorithm for near real-time computations.
  arXiv  Detail & Related papers  (2024-05-15T05:13:20Z)
• SySMOL: Co-designing Algorithms and Hardware for Neural Networks with Heterogeneous Precisions [20.241671088121144]
  Recent quantization techniques have enabled heterogeneous precisions at very fine granularity. These networks require additional hardware to decode the precision settings for individual variables, align the variables, and provide fine-grained mixed-precision compute capabilities. We present an end-to-end co-design approach to efficiently execute networks with fine-grained heterogeneous precisions.
  arXiv  Detail & Related papers  (2023-11-23T17:20:09Z)
• Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff [26.566339984225756]
  Existing salient object detection methods often adopt deeper and wider networks for better performance. We propose a novel trilateral decoder framework by decoupling the U-shape structure into three complementary branches. We show that our method achieves better efficiency-accuracy balance across five benchmarks.
  arXiv  Detail & Related papers  (2023-01-17T03:43:25Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization [71.91942002659795]
  We investigate and adapt network quantization techniques to accelerate inference and enable its use on compute limited platforms. ZippyPoint, our efficient quantized network with binary descriptors, improves the network runtime speed, the descriptor matching speed, and the 3D model size. These improvements come at a minor performance degradation as evaluated on the tasks of homography estimation, visual localization, and map-free visual relocalization.
  arXiv  Detail & Related papers  (2022-03-07T18:59:03Z)
• Multi-Exit Semantic Segmentation Networks [78.44441236864057]
  We propose a framework for converting state-of-the-art segmentation models to MESS networks. specially trained CNNs that employ parametrised early exits along their depth to save during inference on easier samples. We co-optimise the number, placement and architecture of the attached segmentation heads, along with the exit policy, to adapt to the device capabilities and application-specific requirements.
  arXiv  Detail & Related papers  (2021-06-07T11:37:03Z)
• Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform [62.91011959772665]
  Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles. Current approaches that operate directly on the point cloud use complex spatial aggregation operations. We propose a projection-based method, called Multi-scale Interaction Network (MINet), which is very efficient and accurate.
  arXiv  Detail & Related papers  (2020-08-20T19:06:11Z)

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.