LOD-Net: Locality-Aware 3D Object Detection Using Multi-Scale Transformer Network

• URL: http://arxiv.org/abs/2604.16696v1
• Date: Fri, 17 Apr 2026 20:52:20 GMT
• Title: LOD-Net: Locality-Aware 3D Object Detection Using Multi-Scale Transformer Network
• Abstract summary: We propose a novel Multi-Scale Attention (MSA) mechanism integrated into the 3DETR architecture to better capture both local geometry and global context.<n>Our method introduces an upsampling operation that generates high-resolution feature maps, enabling the network to better detect smaller and semantically related objects.
• Score: 16.650772280552115
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 3D object detection in point cloud data remains a challenging task due to the sparsity and lack of global structure inherent in the input. In this work, we propose a novel Multi-Scale Attention (MSA) mechanism integrated into the 3DETR architecture to better capture both local geometry and global context. Our method introduces an upsampling operation that generates high-resolution feature maps, enabling the network to better detect smaller and semantically related objects. Experiments conducted on the ScanNetv2 dataset demonstrate that our 3DETR + MSA model improves detection performance, achieving a gain of almost 1% in mAP@25 and 4.78% in mAP@50 over the baseline. While applying MSA to the 3DETR-m variant shows limited improvement, our analysis reveals the importance of adapting the upsampling strategy for lightweight models. These results highlight the effectiveness of combining hierarchical feature extraction with attention mechanisms in enhancing 3D scene understanding.

Related papers

• TransBridge: Boost 3D Object Detection by Scene-Level Completion with Transformer Decoder [66.22997415145467]
  This paper presents a joint completion and detection framework that improves the detection feature in sparse areas.<n> Specifically, we propose TransBridge, a novel transformer-based up-sampling block that fuses the features from the detection and completion networks.<n>The results show that our framework consistently improves end-to-end 3D object detection, with the mean average precision (mAP) ranging from 0.7 to 1.5 across multiple methods.
  arXiv  Detail & Related papers  (2025-12-12T00:08:03Z)
• Traffic Scene Small Target Detection Method Based on YOLOv8n-SPTS Model for Autonomous Driving [0.0]
  Key issue in autonomous driving is small target recognition in dynamic perception.<n>Existing algorithms suffer from poor detection performance due to missing small target information.<n>We propose an improved YOLOv8n-SPTS model, which enhances the detection accuracy of small traffic targets.
  arXiv  Detail & Related papers  (2025-12-10T03:46:57Z)
• MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection [12.838872442435527]
  Small object detection in UAV imagery is crucial for applications such as search-and-rescue, traffic monitoring, and environmental surveillance.<n>Existing multi-scale fusion methods help, but add computational burden and blur fine details.<n>We propose a unified fusion framework that tightly couples global context with local detail to boost detection performance.
  arXiv  Detail & Related papers  (2025-06-15T02:54:25Z)
• Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection [40.14197775884804]
  MonoASRH is a novel monocular 3D detection framework composed of Efficient Hybrid Feature Aggregation Module (EH-FAM) and Adaptive Scale-Aware 3D Regression Head (ASRH)<n>EH-FAM employs multi-head attention with a global receptive field to extract semantic features for small-scale objects.<n>ASRH encodes 2D bounding box dimensions and then fuses scale features with the semantic features aggregated by EH-FAM.
  arXiv  Detail & Related papers  (2024-11-05T02:33:25Z)
• PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection [59.355022416218624]
  integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. We propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN) PVAFN uses a multi-pooling strategy to integrate both multi-scale and region-specific information effectively.
  arXiv  Detail & Related papers  (2024-08-26T19:43:01Z)
• Hierarchical Point Attention for Indoor 3D Object Detection [111.04397308495618]
  This work proposes two novel attention operations as generic hierarchical designs for point-based transformer detectors. First, we propose Multi-Scale Attention (MS-A) that builds multi-scale tokens from a single-scale input feature to enable more fine-grained feature learning. Second, we propose Size-Adaptive Local Attention (Local-A) with adaptive attention regions for localized feature aggregation within bounding box proposals.
  arXiv  Detail & Related papers  (2023-01-06T18:52:12Z)
• SALISA: Saliency-based Input Sampling for Efficient Video Object Detection [58.22508131162269]
  We propose SALISA, a novel non-uniform SALiency-based Input SAmpling technique for video object detection. We show that SALISA significantly improves the detection of small objects.
  arXiv  Detail & Related papers  (2022-04-05T17:59:51Z)
• The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection [62.1185839286255]
  Low-cost monocular 3D object detection plays a fundamental role in autonomous driving. We introduce a Dynamic Feature Reflecting Network, named DFR-Net. We rank 1st among all the monocular 3D object detectors in the KITTI test set.
  arXiv  Detail & Related papers  (2021-12-28T07:31:18Z)
• Improving 3D Object Detection with Channel-wise Transformer [58.668922561622466]
  We propose a two-stage 3D object detection framework (CT3D) with minimal hand-crafted design. CT3D simultaneously performs proposal-aware embedding and channel-wise context aggregation. It achieves the AP of 81.77% in the moderate car category on the KITTI test 3D detection benchmark.
  arXiv  Detail & Related papers  (2021-08-23T02:03:40Z)
• Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
  arXiv  Detail & Related papers  (2020-08-31T17:10:48Z)

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.