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 1076–1100 of 1576 papers

TitleStatusHype
3DRM:Pair-wise relation module for 3D object detectionCode1
ARM3D: Attention-based relation module for indoor 3D object detectionCode1
3D Object Detection from Images for Autonomous Driving: A SurveyCode1
Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques—0
Semi-supervised 3D Object Detection via Temporal Graph Neural Networks—0
MonoDistill: Learning Spatial Features for Monocular 3D Object DetectionCode1
Survey and Systematization of 3D Object Detection Models and Methods—0
Attention-based Proposals Refinement for 3D Object DetectionCode1
AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection—0
DeepMix: Mobility-aware, Lightweight, and Hybrid 3D Object Detection for Headsets—0
MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm—0
End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and LocalizationCode1
SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object DetectionCode1
Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task—0
SS3D: Sparsely-Supervised 3D Object Detection From Point Cloud—0
Point Cloud Pre-Training With Natural 3D StructuresCode1
Dimension Embeddings for Monocular 3D Object Detection—0
LIFT: Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection—0
Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar NetworkCode1
The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection—0
PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving—0
BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-ViewCode2
EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object DetectionCode1
Revisiting 3D Object Detection From an Egocentric Perspective—0
Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds—0
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All datasetsnuScenesScanNetV2SUN-RGBD valKITTI Cars EasyKITTI Cars HardnuScenes Camera OnlyKITTI Cars Easy valKITTI Cars Moderate valnuscenes Camera-RadarView-of-Delft (val)KITTI Cars Hard valDAIR-V2X-I

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MMFusion-eNDS0.77—Unverified
3MegFusionNDS0.77—Unverified
4RacoonPowerNDS0.76—Unverified
5BEVFusion-eNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8DAANDS0.75—Unverified
9FusionVPENDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified