  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
