Search Results for author:
Found 15 papers, 11 papers with code
EADReg: Probabilistic Correspondence Generation with Efficient Autoregressive Diffusion Model for Outdoor Point Cloud Registration
To address this issue, we propose a novel framework named EADReg for efficient and robust registration of LiDAR point clouds based on autoregressive diffusion models.
Spatiotemporal Decoupling for Efficient Vision-Based Occupancy Forecasting
Existing 3D OCF approaches struggle to predict plausible spatial details for movable objects and suffer from slow inference speeds due to neglecting the bias and uneven distribution of changing occupancy states in both space and time.
GSPR: Multimodal Place Recognition Using 3D Gaussian Splatting for Autonomous Driving
Place recognition is a crucial module to ensure autonomous vehicles obtain usable localization information in GPS-denied environments.
Goal-based Neural Physics Vehicle Trajectory Prediction Model
To address these challenges, this paper proposes a Goal-based Neural Physics Vehicle Trajectory Prediction Model (GNP).
Explicit Interaction for Fusion-Based Place Recognition
Fusion-based place recognition is an emerging technique jointly utilizing multi-modal perception data, to recognize previously visited places in GPS-denied scenarios for robots and autonomous vehicles.
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.
Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications
Furthermore, the standardized evaluation protocol for preset multiple tasks is also provided to compare the performance of all the proposed baselines on present and future occupancy estimation with respect to objects of interest in autonomous driving scenarios.
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.
A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles
Different exploration methods of DRL, including adding action space noise and parameter space noise, are compared against each other in the transfer learning process in this work.
