We introduce a technique for detecting 3D objects and estimating their
position from a single image. Our method is built on top of a similar
state-of-the-art technique [1], but with improved accuracy. The approach
followed in this research first estimates common 3D properties of an object
using a Deep Convolutional Neural Network (DCNN), contrary to other frameworks
that only leverage centre-point predictions. We then combine these estimates
with geometric constraints provided by a 2D bounding box to produce a complete
3D bounding box. The first output of our network estimates the 3D object
orientation using a discrete-continuous loss [1]. The second output predicts
the 3D object dimensions with minimal variance. Here we also present our
extensions by augmenting light-weight feature extractors and a customized
multibin architecture. By combining these estimates with the geometric
constraints of the 2D bounding box, we can accurately (or comparatively)
determine the 3D object pose better than our baseline [1] on the KITTI 3D
detection benchmark [2].

通过改进的深度卷积神经网络以及多元箱子架构和几何限制，本研究在 KITTI 3D 检测基准测试上确定了更准确的三维对象姿态。