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 376–400 of 1576 papers

TitleStatusHype
DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D QueriesCode1
GeoBEV: Learning Geometric BEV Representation for Multi-view 3D Object DetectionCode1
GO-N3RDet: Geometry Optimized NeRF-enhanced 3D Object DetectorCode1
Fusing Event-based and RGB camera for Robust Object Detection in Adverse ConditionsCode1
Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object DetectionCode1
Fully Sparse Fusion for 3D Object DetectionCode1
CoreNet: Conflict Resolution Network for Point-Pixel Misalignment and Sub-Task Suppression of 3D LiDAR-Camera Object DetectionCode1
FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object DetectionCode1
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR RepresentationsCode1
CORE: Cooperative Reconstruction for Multi-Agent PerceptionCode1
3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object DetectionCode1
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDARCode1
Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object DetectionCode1
FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object DetectionCode1
MonoDistill: Learning Spatial Features for Monocular 3D Object DetectionCode1
BirdNet: a 3D Object Detection Framework from LiDAR informationCode1
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty EstimationCode1
BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye ViewCode1
MonoNeRD: NeRF-like Representations for Monocular 3D Object DetectionCode1
FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D DetectionCode1
MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation CoefficientCode1
Boosting 3D Object Detection via Object-Focused Image FusionCode1
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity EstimationCode1
3D Object Detection for Autonomous Driving: A SurveyCode1
Frustum PointNets for 3D Object Detection from RGB-D DataCode1
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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