Accurate 3D object detection is vital in autonomous driving. Traditional LiDAR models struggle with sparse point clouds. We propose a novel approach integrating LiDAR and camera data to maximize sensor strengths while overcoming individual limitations for enhanced 3D object detection. Our research introduces the Channel-wise and Spatially-guided Multimodal feature fusion network (CSMNET) for 3D Object Detection. First, our method enhances LiDAR data by projecting it onto a 2D plane, enabling the extraction of class-specific features from a probability map. Second, we design class-based farthest point sampling (C-FPS), which boosts the selection of foreground points by utilizing point weights based on geometric or probability features while ensuring diversity among the selected points. Third, we developed a parallel attention-based multimodal fusion mechanism achieving higher resolution compared to raw LiDAR points. This fusion mechanism integrates two attention mechanisms: channel attention for LiDAR data and spatial attention for camera data. These mechanisms enhance the utilization of semantic features in a region of interest (ROI) to obtain more representative point features, leading to more effective fusion of information from both LiDAR and camera sources. Specifically, CSMNET achieves an Average Precision (AP) in Bird’s Eye View (BEV) detection of 90.16% (easy), 85.18% (moderate), and 80.51% (hard), with a mean AP (mAP) of 85.12%. In 3D detection, CSMNET attains 82.05% (easy), 72.64% (moderate), and 67.10% (hard) with a mAP of 73.75%. For 2D detection, the scores are 95.47% (easy), 93.25% (moderate), and 86.68% (hard), yielding a mAP of 91.72% for the KITTI dataset.

3D object detection by autonomous vehicles is integral to intelligent transportation. Existing systems often compromise essential foreground point features and local spatial interactions through random down-sampling, focusing primarily on local feature extraction. However, this neglects interactions among distant yet significant points, limiting semantic information and detection performance due to inherent point cloud data sparsity. Addressing this, our proposed Geometric Feature Aggregation and Self-Attention in a Multi-Head Transformer (GFA-SMT) architecture leverages Graph Convolutional Networks and multi-channel transformers to enhance weak semantic information of distant sparse objects. GFA-SMT comprises three modules: Distance Suppression for Local Receptive Fields (DsLRF), Geometric Feature Aggregator with Multi-head Self Attention (GFaSA), and Predicted Key-point Weighting and Refinement (PKwR). DsLRF preserves foreground features, GFaSA encodes similar features and aggregates edge features, while PKwR focuses on key-points for enhancing geometric knowledge of distant and sparse objects. Extensive experiments on KITTI, DIARV2X-I and NuScenes datasets show significant enhancements in widely used techniques, resulting in notable increases in average precision (AP) for 3D object detection: 4.08%, 5.56%, and 4.62%, respectively, on the KITTI test dataset. GFA-SMT enhances point cloud detection accuracy, particularly at medium and long distances, with minimal impact on run-time performance and model parameters.