Diffusion-based 3D Object Detection with Random Boxes
Xin Zhou Jinghua Hou Tingting Yao Dingkang Liang Zhe Liu Zhikang Zou Xiaoqing Ye Jianwei Cheng Xiang Bai
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.