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Monocular 3D Object Detection

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.

Papers

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Showing 51–100 of 187 papers

TitleStatusHype
MonoNeRD: NeRF-like Representations for Monocular 3D Object DetectionCode1
MonoNext: A 3D Monocular Object Detection with ConvNext—0
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning—0
Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection—0
Weakly Supervised 3D Object Detection with Multi-Stage Generalization—0
VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D AnnotationsCode0
Monocular 2D Camera-based Proximity Monitoring for Human-Machine Collision Warning on Construction SitesCode0
Learning Occupancy for Monocular 3D Object DetectionCode1
MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes—0
SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object DetectionCode1
Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver—0
SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision TransformerCode1
Learning to Zoom and Unzoom—0
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation—0
MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer—0
Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection—0
Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction ConsistencyCode0
MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts—0
MonoEdge: Monocular 3D Object Detection Using Local Perspectives—0
3DPPE: 3D Point Positional Encoding for Transformer-based Multi-Camera 3D Object DetectionCode1
Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference—0
OBMO: One Bounding Box Multiple Objects for Monocular 3D Object DetectionCode0
3D Object Aided Self-Supervised Monocular Depth Estimation—0
IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection—0
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object DetectionCode0
3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection TransformersCode1
Cross-Modality Knowledge Distillation Network for Monocular 3D Object DetectionCode1
Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection—0
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection—0
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision—0
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection—0
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence MapsCode2
A Simple Baseline for Multi-Camera 3D Object DetectionCode1
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object DetectionCode0
Aerial Monocular 3D Object DetectionCode0
Monocular 3D Object Detection with Depth from MotionCode2
DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object DetectionCode2
Densely Constrained Depth Estimator for Monocular 3D Object DetectionCode1
DID-M3D: Decoupling Instance Depth for Monocular 3D Object DetectionCode1
Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection—0
Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object DetectionCode1
Self-Supervised 3D Monocular Object Detection by Recycling Bounding Boxes—0
MonoGround: Detecting Monocular 3D Objects from the GroundCode1
Delving into the Pre-training Paradigm of Monocular 3D Object Detection—0
Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving—0
Towards Model Generalization for Monocular 3D Object Detection—0
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection—0
Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training—0
Shape-Aware Monocular 3D Object Detection—0
Homography Loss for Monocular 3D Object DetectionCode1
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