  Low-cost autonomous agents including autonomous driving vehicles chiefly
adopt monocular 3D object detection to perceive surrounding environment. This
paper studies 3D intermediate representation methods which generate
intermediate 3D features for subsequent tasks. For example, the 3D features can
be taken as input for not only detection, but also end-to-end prediction and/or
planning that require a bird's-eye-view feature representation. In the study,
we found that in generating 3D representation previous methods do not maintain
the consistency between the objects' implicit poses in the latent space,
especially orientations, and the explicitly observed poses in the Euclidean
space, which can substantially hurt model performance. To tackle this problem,
we present a novel monocular detection method, the first one being aware of the
poses to purposefully guarantee that they are consistent between the implicit
and explicit features. Additionally, we introduce a local ray attention
mechanism to efficiently transform image features to voxels at accurate 3D
locations. Thirdly, we propose a handcrafted Gaussian positional encoding
function, which outperforms the sinusoidal encoding function while retaining
the benefit of being continuous. Results show that our method improves the
state-of-the-art 3D intermediate representation method by 3.15%. We are ranked
1st among all the reported monocular methods on both 3D and BEV detection
benchmark on KITTI leaderboard as of th result's submission time.
