3D object detection is an essential task for achieving autonomous driving. Existing anchor-based detection methods rely on empirical heuristics setting of anchors, which makes the algorithms lack elegance. In recent years, we have witnessed the rise of several generative models, among which diffusion models show great potential for learning the transformation of two distributions. Our proposed Diff3Det migrates the diffusion model to proposal generation for 3D object detection by considering the detection boxes as generative targets. During training, the object boxes diffuse from the ground truth boxes to the Gaussian distribution, and the decoder learns to reverse this noise process. In the inference stage, the model progressively refines a set of random boxes to the prediction results. We provide detailed experiments on the KITTI benchmark and achieve promising performance compared to classical anchor-based 3D detection methods.

翻译：三维目标检测是实现自动驾驶的关键任务。现有基于锚点的检测方法依赖于锚点的经验性启发式设置，导致算法缺乏严谨性。近年来，多种生成模型相继兴起，其中扩散模型在学习两种分布转换方面展现出巨大潜力。我们提出的Diff3Det方法将扩散模型迁移至三维目标检测的候选框生成阶段，将检测框视为生成目标。在训练过程中，目标框从真实标注框扩散至高斯分布，解码器学习逆转这一噪声过程。在推理阶段，模型逐步将一组随机框优化为预测结果。我们在KITTI数据集上进行了详细实验，与经典基于锚点的三维检测方法相比，取得了具有竞争力的性能表现。