GraphFusion3D: Dynamic Graph Attention Convolution with Adaptive Cross-Modal Transformer for 3D Object Detection
This work addresses 3D object detection for robotics and autonomous systems, showing incremental improvements through multi-modal fusion and graph-based refinement.
The paper tackled the challenges of sparse data and incomplete structures in 3D object detection by proposing GraphFusion3D, which integrates image features and models neighborhood relationships, achieving 70.6% AP25 and 51.2% AP50 on SUN RGB-D and 75.1% AP25 and 60.8% AP50 on ScanNetV2.
Despite significant progress in 3D object detection, point clouds remain challenging due to sparse data, incomplete structures, and limited semantic information. Capturing contextual relationships between distant objects presents additional difficulties. To address these challenges, we propose GraphFusion3D, a unified framework combining multi-modal fusion with advanced feature learning. Our approach introduces the Adaptive Cross-Modal Transformer (ACMT), which adaptively integrates image features into point representations to enrich both geometric and semantic information. For proposal refinement, we introduce the Graph Reasoning Module (GRM), a novel mechanism that models neighborhood relationships to simultaneously capture local geometric structures and global semantic context. The module employs multi-scale graph attention to dynamically weight both spatial proximity and feature similarity between proposals. We further employ a cascade decoder that progressively refines detections through multi-stage predictions. Extensive experiments on SUN RGB-D (70.6\% AP$_{25}$ and 51.2\% AP$_{50}$) and ScanNetV2 (75.1\% AP$_{25}$ and 60.8\% AP$_{50}$) demonstrate a substantial performance improvement over existing approaches.