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Found 4 papers, 1 papers with code
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss
However, image-to point representation learning for autonomous driving datasets faces two main challenges: 1) the abundance of self-similarity, which results in the contrastive losses pushing away semantically similar point and image regions and thus disturbing the local semantic structure of the learned representations, and 2) severe class imbalance as pretraining gets dominated by over-represented classes.
Point Density-Aware Voxels for LiDAR 3D Object Detection
LiDAR has become one of the primary 3D object detection sensors in autonomous driving.
Dense Voxel Fusion for 3D Object Detection
Sequential fusion methods suffer from a limited number of pixel and point correspondences due to point cloud sparsity, or their performance is strictly capped by the detections of one of the modalities.
Pattern-Aware Data Augmentation for LiDAR 3D Object Detection
Autonomous driving datasets are often skewed and in particular, lack training data for objects at farther distances from the ego vehicle.
