5 accepted papers
Recent top-performing temporal 3D detectors based on Lidars have increasingly adopted region-based paradigms. They first generate coarse proposals, followed by encoding and fusing regional features. However, indiscriminate sampling and fusion often overlook the varying contributions of individual po…
Multimodal 3D occupancy prediction has garnered significant attention for its potential in autonomous driving. However, most existing approaches are single-modality: camera-based methods lack depth information, while LiDAR-based methods struggle with occlusions. Current lightweight methods primarily…
Multi-sensor collaborative perception has been a significant trend in self-driving and robot navigation. The precondition for multi-sensor fusion is the accurate calibration between sensors. Traditional LiDAR-Camera calibrations rely on laborious manual operations. Several recent studies have demons
In autonomous driving, the LiDAR-Camera system plays a crucial role in a vehicle’s perception of 3D environments. To effectively fuse information from both camera and LiDAR, extrinsic calibration is indispensable. Recently, some researchers have proposed deep learning-based methods that utilize conv…
Point clouds play a crucial role in robots and intelligent vehicles. Noise caused by fog droplets seriously degrades the quality of point clouds. Previous researches have shown that the extent of degradation is correlated with visibility. The fog attenuation coefficient is associated with visibility…