量子逆上下文视觉变换器（Q-ICVT）：自动驾驶车辆3D目标检测的新前沿
Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVs
AI总结：
针对自动驾驶中远距离目标检测的融合挑战，提出Q-ICVT两阶段融合方法，结合绝热计算的可逆变换器GAT和局部融合模块SELF，在Waymo数据集上mAPH达82.54，较现有最优提升1.88%。
摘要：
自动驾驶汽车（AV）领域主要利用LiDAR和摄像头数据的多模态集成，与使用单一模态相比，可获得更好的性能。然而，由于摄像头的高分辨率与LiDAR的稀疏数据之间存在差异，融合过程在检测远距离物体时面临挑战。全局视角与局部细节的整合不足导致融合性能欠佳。为解决此问题，我们开发了一种创新的两阶段融合过程，称为量子逆上下文视觉变换器（Q-ICVT）。该方法利用量子概念中的绝热计算，创建了一种新颖的可逆视觉变换器，即全局绝热变换器（GAT）。GAT以全局形式将稀疏的LiDAR特征与密集图像中的语义特征聚合，用于跨模态集成。此外，局部融合稀疏专家（SELF）模块将稀疏的LiDAR 3D提议映射，并使用门控点融合方法将原始点云的位置信息编码到密集的相机特征空间。我们的实验表明，Q-ICVT在Waymo数据集上针对L2难度实现了82.54的mAPH，比当前最先进的融合方法提高了1.88%。我们还通过消融研究分析了GAT和SELF，以突出Q-ICVT的影响。我们的代码可在https://github.com/sanjay-810/Qicvt获取。
英文摘要：
The field of autonomous vehicles (AVs) predominantly leverages multi-modal integration of LiDAR and camera data to achieve better performance compared to using a single modality. However, the fusion process encounters challenges in detecting distant objects due to the disparity between the high resolution of cameras and the sparse data from LiDAR. Insufficient integration of global perspectives with local-level details results in sub-optimal fusion performance.To address this issue, we have developed an innovative two-stage fusion process called Quantum Inverse Contextual Vision Transformers (Q-ICVT). This approach leverages adiabatic computing in quantum concepts to create a novel reversible vision transformer known as the Global Adiabatic Transformer (GAT). GAT aggregates sparse LiDAR features with semantic features in dense images for cross-modal integration in a global form. Additionally, the Sparse Expert of Local Fusion (SELF) module maps the sparse LiDAR 3D proposals and encodes position information of the raw point cloud onto the dense camera feature space using a gating point fusion approach. Our experiments show that Q-ICVT achieves an mAPH of 82.54 for L2 difficulties on the Waymo dataset, improving by 1.88% over current state-of-the-art fusion methods. We also analyze GAT and SELF in ablation studies to highlight the impact of Q-ICVT. Our code is available at https://github.com/sanjay-810/Qicvt Q-ICVT
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
