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

2026-04-17

AI Summary

This work addresses the degraded performance in detecting small objects and semantically related entities caused by point cloud sparsity and the lack of global structural context. Building upon the 3DETR framework, the authors introduce a multi-scale attention (MSA) mechanism combined with a feature upsampling strategy to effectively integrate local geometric details with global contextual information. This approach not only enhances the model’s local perception capabilities but also reveals that lightweight architectures require carefully matched upsampling strategies to achieve optimal performance. Evaluated on the ScanNetv2 dataset, the proposed method achieves a nearly 1% improvement in mAP@25 and a significant 4.78% gain in mAP@50, substantially outperforming the baseline model.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• 3D object detection
• point cloud
• sparsity
• global structure
• local geometry

Innovation

• Multi-Scale Attention
• 3D Object Detection
• Point Cloud
• 3DETR
• Upsampling