The KITTI Vision Benchmark Suite

Abstract— In this work, we propose a novel one-stage and
keypoints-based framework for monocular 3D objects detection
using only RGB images, called KM3D-Net. 2D detection only
requires a deep neural network to predict 2D properties of
objects, as it is a semanticity-aware task. For image-based 3D
detection, we argue that the combination of the deep neural
network and geometric constraints are needed to estimate
appearance-related and spatial-related information synergisti-
cally. Here, we design a fully convolutional model to predict
object keypoints, dimension, and orientation and then combine
these estimations with perspective geometry constraints to com-
pute position attributes. Further, we reformulate the geometric
constraints as a differentiable version and embed it into the net-
work to reduce running time while maintaining the consistency
of model outputs in an end-to-end fashion. Benefiting from this
simple structure, we then propose an effective semi-supervised
training strat

DLA-34

@misc{2009.00764,
Author = {Peixuan Li},
Title = {Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training},
Year = {2020},
Eprint = {arXiv:2009.00764},
}