3D Object Detection From Monocular Images

5 papers with code • 2 benchmarks • 2 datasets

This is the task of detecting 3D objects from monocular images (as opposed to LiDAR based counterparts). It is usually associated with autonomous driving based tasks.

( Image credit: Orthographic Feature Transform for Monocular 3D Object Detection )

Most implemented papers

M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

garrickbrazil/M3D-RPN ICCV 2019

Understanding the world in 3D is a critical component of urban autonomous driving.

Orthographic Feature Transform for Monocular 3D Object Detection

tom-roddick/oft 20 Nov 2018

This allows us to reason holistically about the spatial configuration of the scene in a domain where scale is consistent and distances between objects are meaningful.

Geometry Uncertainty Projection Network for Monocular 3D Object Detection

supermhp/gupnet ICCV 2021

In this paper, we propose a Geometry Uncertainty Projection Network (GUP Net) to tackle the error amplification problem at both inference and training stages.

ROCA: Robust CAD Model Retrieval and Alignment from a Single Image

cangumeli/ROCA CVPR 2022

We present ROCA, a novel end-to-end approach that retrieves and aligns 3D CAD models from a shape database to a single input image.

DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection

abhi1kumar/deviant 21 Jul 2022

As a result, DEVIANT is equivariant to the depth translations in the projective manifold whereas vanilla networks are not.