Monocular 3D object detection aims to localize 3D bounding boxes in an input single 2D
image. It is a highly challenging problem and remains open, especially when no extra …

Monocular 3D object detection plays an important role in autonomous driving and still
remains challenging. To achieve fast and accurate monocular 3D object detection, we …

Monocular 3D object detection task aims to predict the 3D bounding boxes of objects based
on monocular RGB images. Since the location recovery in 3D space is quite difficult on …

Mobile monocular 3D object detection (Mono3D)(eg, on a vehicle, a drone, or a robot) is an
important yet challenging task. Existing transformer-based offline Mono3D models adopt …

Semi-supervised learning (SSL) has promising potential for improving model performance
using both labelled and unlabelled data. Since recovering 3D information from 2D images is …

Monocular 3D detection of vehicle and infrastructure sides are two important topics in
autonomous driving. Due to diverse sensor installations and focal lengths, researchers are …

Monocular 3D object detection is well-known to be a challenging vision task due to the loss
of depth information; attempts to recover depth using separate image-only approaches lead …

Low-cost monocular 3D object detection plays a fundamental role in autonomous driving,
whereas its accuracy is still far from satisfactory. Our objective is to dig into the 3D object …

In this paper we introduce a method for multi-class, monocular 3D object detection from a
single RGB image, which exploits a novel disentangling transformation and a novel, self …

In this paper, we propose to learn a deep fitting degree scoring network for monocular 3D
object detection, which aims to score fitting degree between proposals and object …