In recent times, there has been a notable surge in multimodal approaches that
decorates raw LiDAR point clouds with camera-derived features to improve object
detection performance. However, we found that these methods still grapple with
the inherent sparsity of LiDAR point cloud data, primarily because fewer points
are enriched with camera-derived features for sparsely distributed objects. We
present an innovative approach that involves the generation of virtual LiDAR
points using camera images and enhancing these virtual points with semantic
labels obtained from image-based segmentation networks to tackle this issue and
facilitate the detection of sparsely distributed objects, particularly those
that are occluded or distant. Furthermore, we integrate a distance aware data
augmentation (DADA) technique to enhance the models capability to recognize
these sparsely distributed objects by generating specialized training samples.
Our approach offers a versatile solution that can be seamlessly integrated into
various 3D frameworks and 2D semantic segmentation methods, resulting in
significantly improved overall detection accuracy. Evaluation on the KITTI and
nuScenes datasets demonstrates substantial enhancements in both 3D and birds
eye view (BEV) detection benchmarks

通过使用基于相机图像生成虚拟 LiDAR 点，并利用图像分割网络获得的语义标签增强这些虚拟点，以解决稀疏分布的、被遮挡或远离的对象检测问题，并通过生成专门的训练样本的距离感知数据增强技术，将这种方法无缝地整合到各种三维框架和二维语义分割方法中，从而显著提高整体检测准确性，并在 KITTI 和 nuScenes 数据集上的评估中展示了在三维和鸟瞰视角检测基准上的重大改进。