  Monocular 3D object detection poses a significant challenge in 3D scene
understanding due to its inherently ill-posed nature in monocular depth
estimation. Existing methods heavily rely on supervised learning using abundant
3D labels, typically obtained through expensive and labor-intensive annotation
on LiDAR point clouds. To tackle this problem, we propose a novel weakly
supervised 3D object detection framework named VSRD (Volumetric Silhouette
Rendering for Detection) to train 3D object detectors without any 3D
supervision but only weak 2D supervision. VSRD consists of multi-view 3D
auto-labeling and subsequent training of monocular 3D object detectors using
the pseudo labels generated in the auto-labeling stage. In the auto-labeling
stage, we represent the surface of each instance as a signed distance field
(SDF) and render its silhouette as an instance mask through our proposed
instance-aware volumetric silhouette rendering. To directly optimize the 3D
bounding boxes through rendering, we decompose the SDF of each instance into
the SDF of a cuboid and the residual distance field (RDF) that represents the
residual from the cuboid. This mechanism enables us to optimize the 3D bounding
boxes in an end-to-end manner by comparing the rendered instance masks with the
ground truth instance masks. The optimized 3D bounding boxes serve as effective
training data for 3D object detection. We conduct extensive experiments on the
KITTI-360 dataset, demonstrating that our method outperforms the existing
weakly supervised 3D object detection methods. The code is available at
https://github.com/skmhrk1209/VSRD.
