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Found 6 papers, 4 papers with code
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving
In this paper, we address this challenge by introducing a world model-based autonomous driving 4D representation learning framework, dubbed \emph{DriveWorld}, which is capable of pre-training from multi-camera driving videos in a spatio-temporal fashion.
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting
Finally, for MC3D-Det joint training, the elaborate dataset merge strategy is designed to solve the problem of inconsistent camera numbers and camera parameters.
Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions
Robust 3D perception under corruption has become an essential task for the realm of 3D vision.
TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning
In this paper, we propose the learning scheme of Target Inner-Geometry from the LiDAR modality into camera-based BEV detectors for both dense depth and BEV features, termed as TiG-BEV.
FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection
Existing 3D detectors significantly improve the accuracy by adopting a two-stage paradigm which merely relies on LiDAR point clouds for 3D proposal refinement.
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders
This work proposes a solution to reduce the dependence on labelled 3D training data by leveraging pre-training on large-scale unlabeled outdoor LiDAR point clouds using masked autoencoders (MAE).
