SOTAVerified

3D Object Detection

3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.

( Image credit: AVOD )

Papers

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Showing 601–650 of 1576 papers

TitleStatusHype
MonoNext: A 3D Monocular Object Detection with ConvNext—0
NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object DetectionCode2
Spatio-Temporal Domain Awareness for Multi-Agent Collaborative PerceptionCode1
HeightFormer: Explicit Height Modeling without Extra Data for Camera-only 3D Object Detection in Bird's Eye View—0
PG-RCNN: Semantic Surface Point Generation for 3D Object DetectionCode1
DFA3D: 3D Deformable Attention For 2D-to-3D Feature LiftingCode0
R2Det: Redemption from Range-view for Accurate 3D Object Detection—0
SA-BEV: Generating Semantic-Aware Bird's-Eye-View Feature for Multi-view 3D Object DetectionCode1
HVDetFusion: A Simple and Robust Camera-Radar Fusion FrameworkCode1
CORE: Cooperative Reconstruction for Multi-Agent PerceptionCode1
SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar—0
Improving Online Lane Graph Extraction by Object-Lane Clustering—0
MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection—0
RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object DetectionCode1
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving—0
Ada3D : Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection—0
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning—0
S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality—0
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection—0
Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-LabelingCode1
SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection—0
Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object DetectionCode1
SUIT: Learning Significance-guided Information for 3D Temporal Detection—0
LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion—0
SSC3OD: Sparsely Supervised Collaborative 3D Object Detection from LiDAR Point Clouds—0
Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object DetectionCode1
GMM: Delving into Gradient Aware and Model Perceive Depth Mining for Monocular 3D Detection—0
Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithmsCode0
Tame a Wild Camera: In-the-Wild Monocular Camera CalibrationCode1
Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection—0
Frame Fusion with Vehicle Motion Prediction for 3D Object Detection—0
Predict to Detect: Prediction-guided 3D Object Detection using Sequential ImagesCode1
Towards a Robust Sensor Fusion Step for 3D Object Detection on Corrupted DataCode0
Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine PerceptionCode2
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point CloudsCode2
Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization—0
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio MaskingCode0
Weakly Supervised 3D Object Detection with Multi-Stage Generalization—0
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud SequencesCode1
Multi-View Representation is What You Need for Point-Cloud Pre-Training—0
SAM3D: Zero-Shot 3D Object Detection via Segment Anything ModelCode2
OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection—0
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception—0
Doubly Robust Self-TrainingCode0
Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color ContrastCode1
UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous DrivingCode2
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
View-to-Label: Multi-View Consistency for Self-Supervised 3D Object Detection—0
Radar Enlighten the Dark: Enhancing Low-Visibility Perception for Automated Vehicles with Camera-Radar FusionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MegFusionNDS0.77—Unverified
3MMFusion-eNDS0.77—Unverified
4BEVFusion-eNDS0.76—Unverified
5RacoonPowerNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8FusionVPENDS0.75—Unverified
9FocalFormer3D-FNDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified