1

A novel multimodal 3D object detection algorithm, IS-Fusion, is presented in this work. It enhances the previous multimodal BEV representation by incorporating instance-level information, which significantly improves detection performance. IS-Fusion achieves the best performance among the published works to date.

We propose a new collaborative 3D object detection algorithm (DI-V2X) aimed at minimizing the domain discrepancy of the input data from various sources, including vehicles and infrastructure. Our approach features three key modules, the Domain-Mixing Instance Augmentation (DMA) module, the Progressive Domain-Invariant Distillation (PDD) module, and the Domain-Adaptive Fusion (DAF) module. DI-V2X achieves the best performance in V2X perception.

We present a novel learning paradigm, LWSIS, that inherits the fruits of off-the-shelf 3D point cloud to guide the training of 2D instance segmentation models to save mask-level annotations. We advocate a new dataset nuInsSeg based on nuScenes to extend existing 3D LiDAR annotations with 2D image segmentation annotations.

We prsent SSDA3D in this work, which is the first effort for semi-supervised domain adaptation in the context of 3D object detection. SSDA3D is achieved by a novel framework that jointly addresses inter-domain adaptation and intra-domain generalization.

We propose a proposal-level point cloud SSL framework, named ProposalContrast, that conducts proposal-wise contrastive pre-training for detection-aligned representation learning. We comprehensively demonstrate the generalizability of our model across diverse 3D detector architectures and datasets.

We propose ProficientTeachers for LiDAR-based 3D object detection, which is achieved by promoting the plain teacher model to proficient teachers inspired by ensemble learning. Our framework not only performs better results, but also removes the necessity of confidencebased thresholds for filtering pseudo labels.

We integrates singl object tracking and metric learning into a unified triplet network via multi-task learning. A task-specific attention module is also presented to address th specific nature of each task. Experimental results show that it achieves promising performance on several MOT benchmarks.

We propose a new LiDAR-based 3D video object detector that leverages the previous long-term information to improve the 3D detection performance. Extensive evaluations demonstrate that our 3D video object detector achieves better performance against the single-frame detectors.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.