In this technical report, we present our solution, dubbed MV-FCOS3D++, for
the Camera-Only 3D Detection track in Waymo Open Dataset Challenge 2022. For
multi-view camera-only 3D detection, methods based on bird-eye-view or 3D
geometric representations can leverage the stereo cues from overlapped regions
between adjacent views and directly perform 3D detection without hand-crafted
post-processing. However, it lacks direct semantic supervision for 2D
backbones, which can be complemented by pretraining simple monocular-based
detectors. Our solution is a multi-view framework for 4D detection following
this paradigm. It is built upon a simple monocular detector FCOS3D++,
pretrained only with object annotations of Waymo, and converts multi-view
features to a 3D grid space to detect 3D objects thereon. A dual-path neck for
single-frame understanding and temporal stereo matching is devised to
incorporate multi-frame information. Our method finally achieves 49.75% mAPL
with a single model and wins 2nd place in the WOD challenge, without any
LiDAR-based depth supervision during training. The code will be released at
this https URL

该技术报告介绍了我们的解决方案 MV-FCOS3D ++，用于 Waymo Open Dataset Challenge 2022 中的仅相机 3D 检测研究赛题，它是一个基于多视角相机的 3D 检测多视图框架，采用 FCOS3D ++ 进行预训练，并将多视角特征转换为 3D 网格空间，以检测 3D 对象，其具备单帧理解及时间立体匹配的双通道项以纳入多帧信息。使用该模型在没有任何 LiDAR 深度监督的情况下，达到了 49.75％的 mAPL 评价指数，最终以单模型获得第二名。