  Despite monocular 3D object detection having recently made a significant leap
forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR
recovery, such two-stage methods typically suffer from overfitting and are
incapable of explicitly encapsulating the geometric relation between depth and
object bounding box. To overcome this limitation, we instead propose OPA-3D, a
single-stage, end-to-end, Occlusion-Aware Pixel-Wise Aggregation network that
to jointly estimate dense scene depth with depth-bounding box residuals and
object bounding boxes, allowing a two-stream detection of 3D objects, leading
to significantly more robust detections. Thereby, the geometry stream denoted
as the Geometry Stream, combines visible depth and depth-bounding box residuals
to recover the object bounding box via explicit occlusion-aware optimization.
In addition, a bounding box based geometry projection scheme is employed in an
effort to enhance distance perception. The second stream, named as the Context
Stream, directly regresses 3D object location and size. This novel two-stream
representation further enables us to enforce cross-stream consistency terms
which aligns the outputs of both streams, improving the overall performance.
Extensive experiments on the public benchmark demonstrate that OPA-3D
outperforms state-of-the-art methods on the main Car category, whilst keeping a
real-time inference speed. We plan to release all codes and trained models
soon.
