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Found 6 papers, 6 papers with code
PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environments
With extensive experiments, PC-NeRF is proven to achieve high-precision novel LiDAR view synthesis and 3D reconstruction in large-scale scenes.
LCPR: A Multi-Scale Attention-Based LiDAR-Camera Fusion Network for Place Recognition
However, most existing multimodal place recognition methods only use limited field-of-view camera images, which leads to an imbalance between features from different modalities and limits the effectiveness of sensor fusion.
PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data Loss in Autonomous Driving Environments
Reconstructing large-scale 3D scenes is essential for autonomous vehicles, especially when partial sensor data is lost.
PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction
In this letter, we propose a novel efficient Transformer-based network to predict the future LiDAR point clouds exploiting the past point cloud sequences.
CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data
LiDAR-based place recognition (LPR) is one of the most crucial components of autonomous vehicles to identify previously visited places in GPS-denied environments.
SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data
It uses multi-scale transformers to generate a global descriptor for each sequence of LiDAR range images in an end-to-end fashion.
