Monocular 3D Object Detection

78 papers with code • 15 benchmarks • 6 datasets

Monocular 3D Object Detection is the task to draw 3D bounding box around objects in a single 2D RGB image. It is localization task but without any extra information like depth or other sensors or multiple-images.


Use these libraries to find Monocular 3D Object Detection models and implementations

Most implemented papers

FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection

open-mmlab/mmdetection3d 22 Apr 2021

In this paper, we study this problem with a practice built on a fully convolutional single-stage detector and propose a general framework FCOS3D.

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.

SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation

lzccccc/SMOKE 24 Feb 2020

Estimating 3D orientation and translation of objects is essential for infrastructure-less autonomous navigation and driving.

Objects are Different: Flexible Monocular 3D Object Detection

zhangyp15/MonoFlex CVPR 2021

The precise localization of 3D objects from a single image without depth information is a highly challenging problem.

ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection

saic-vul/imvoxelnet 2 Jun 2021

To address this problem, we propose ImVoxelNet, a novel fully convolutional method of 3D object detection based on monocular or multi-view RGB images.

Learning Depth-Guided Convolutions for Monocular 3D Object Detection

dingmyu/D4LCN CVPR 2020

3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information.

Kinematic 3D Object Detection in Monocular Video

Nicholasli1995/EgoNet ECCV 2020

In this work, we propose a novel method for monocular video-based 3D object detection which carefully leverages kinematic motion to improve precision of 3D localization.

Categorical Depth Distribution Network for Monocular 3D Object Detection


We validate our approach on the KITTI 3D object detection benchmark, where we rank 1st among published monocular methods.

Is Pseudo-Lidar needed for Monocular 3D Object detection?

tri-ml/dd3d ICCV 2021

Recent progress in 3D object detection from single images leverages monocular depth estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors.

Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR

AutoAILab/FusionDepth 20 Sep 2021

Unlike the existing methods that use sparse LiDAR mainly in a manner of time-consuming iterative post-processing, our model fuses monocular image features and sparse LiDAR features to predict initial depth maps.