The KITTI Vision Benchmark Suite

In this work, to accurately capture detailed
features in the scene and avoid interference of
inaccurate depth information, we propose a
monocular 3D object detection method with a novel
decoupled encoder-decoder framework MonoCDiT,
which introduces asymmetric convolution to
transformer with decoupled depth features and
visual features. The convolution encoder
aggregates local features of the input image and
encodes the features using depth-wise convolution
with multiple convolutional kernels of different
shapes to obtain more details in the image. The
decoupled structure allows visual and depth
features to learn independently without
interfering with each other. In addition, the
combination of convolution and transformer
enables MonoCDiT to focus on both local and global
features.

DLA-102 + 2 conv encoders + 2 transformer encoders +
2 transformer decoders + 1 fusion module