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 1161–1170 of 1576 papers

TitleStatusHype
Learned Multimodal Compression for Autonomous Driving—0
Learning 3D Perception from Others' Predictions—0
Learning Class Prototypes for Unified Sparse-Supervised 3D Object Detection—0
Multi-View Representation is What You Need for Point-Cloud Pre-Training—0
Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels—0
Learning Temporal Cues by Predicting Objects Move for Multi-camera 3D Object Detection—0
Learning to Evaluate Perception Models Using Planner-Centric Metrics—0
Learning to Zoom and Unzoom—0
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection—0
Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection—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
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