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 1031–1040 of 1576 papers

TitleStatusHype
mmFUSION: Multimodal Fusion for 3D Objects Detection—0
MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review—0
Hierarchical Point Attention for Indoor 3D Object Detection—0
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation—0
MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer—0
MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation—0
MonoCoP: Chain-of-Prediction for Monocular 3D Object Detection—0
MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models—0
Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss—0
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach—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