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Found 8 papers, 6 papers with code
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).
UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction
When compared to monocular pre-training methods on the nuScenes dataset, UniScene shows a significant improvement of about 2. 0% in mAP and 2. 0% in NDS for multi-camera 3D object detection, as well as a 3% increase in mIoU for surrounding semantic scene completion.
Attentional Graph Neural Network for Parking-slot Detection
Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection.
ORFD: A Dataset and Benchmark for Off-Road Freespace Detection
Freespace detection is an essential component of autonomous driving technology and plays an important role in trajectory planning.
UniWorld: Autonomous Driving Pre-training via World Models
In this paper, we draw inspiration from Alberto Elfes' pioneering work in 1989, where he introduced the concept of the occupancy grid as World Models for robots.
Trajectory Prediction for Autonomous Driving with Topometric Map
The experimental results show that the proposed method outperforms state-of-the-art multimodal methods and is robust to the perturbations of the topometric map.
Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds
3D moving object detection is one of the most critical tasks in dynamic scene analysis.
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization
In this work, we show that by simply applying consistency training with random data augmentation, state-of-the-art results on domain adaptation (DA) and generalization (DG) can be obtained.
