As a crucial task of autonomous driving, 3D object detection has made great
progress in recent years. However, monocular 3D object detection remains a
challenging problem due to the unsatisfactory performance in depth estimation.
Most existing monocular methods typically directly regress the scene depth
while ignoring important relationships between the depth and various geometric
elements (e.g. bounding box sizes, 3D object dimensions, and object poses). In
this paper, we propose to learn geometry-guided depth estimation with
projective modeling to advance monocular 3D object detection. Specifically, a
principled geometry formula with projective modeling of 2D and 3D depth
predictions in the monocular 3D object detection network is devised. We further
implement and embed the proposed formula to enable geometry-aware deep
representation learning, allowing effective 2D and 3D interactions for boosting
the depth estimation. Moreover, we provide a strong baseline through addressing
substantial misalignment between 2D annotation and projected boxes to ensure
robust learning with the proposed geometric formula. Experiments on the KITTI
dataset show that our method remarkably improves the detection performance of
the state-of-the-art monocular-based method without extra data by 2.80% on the
moderate test setting. The model and code will be released at
this https URL

本篇研究提出一种基于几何建模的投影模型方法，用于改进单目三维物体检测中的深度估计问题，实验证明该方法使得检测性能得到了显著提升。

利用投影建模学习基于几何引导的深度，实现单目三维物体检测

Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D  Object Detection

Existing optical flow methods make generic, spatially homogeneous,
assumptions about the spatial structure of the flow. In reality, optical flow
varies across an image depending on object class. Simply put, different objects
move differently. Here we exploit recent advances in static semantic scene
segmentation to segment the image into objects of different types. We define
different models of image motion in these regions depending on the type of
object. For example, we model the motion on roads with homographies, vegetation
with spatially smooth flow, and independently moving objects like cars and
planes with affine motion plus deviations. We then pose the flow estimation
problem using a novel formulation of localized layers, which addresses
limitations of traditional layered models for dealing with complex scene
motion. Our semantic flow method achieves the lowest error of any published
monocular method in the KITTI-2015 flow benchmark and produces qualitatively
better flow and segmentation than recent top methods on a wide range of natural
videos.

利用静态语义场景分割技术提高光流方法中对于不同物体运动的预测准确度，并通过引入局部化分层模型来解决在复杂场景中估算光流的问题，达到了 KITTI-2015 流量基准测试中最低误差和更好的分割效果及流畅度。