Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. Conventional …
P Xu, X Zhu, DA Clifton - IEEE Transactions on Pattern Analysis …, 2023 - ieeexplore.ieee.org
Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications …
Multi-sensor fusion is essential for an accurate and reliable autonomous driving system. Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with …
Abstract 3D object detectors usually rely on hand-crafted proxies, eg, anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …
Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to …
We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. Existing state-of-the-art BEV …
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are …
How should we integrate representations from complementary sensors for autonomous driving? Geometry-based fusion has shown promise for perception (eg, object detection …
X Lai, Y Chen, F Lu, J Liu, J Jia - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from …