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 1051–1060 of 1576 papers

TitleStatusHype
MonoDETRNext: Next-Generation Accurate and Efficient Monocular 3D Object Detector—0
MonoDiff: Monocular 3D Object Detection and Pose Estimation with Diffusion Models—0
MonoEdge: Monocular 3D Object Detection Using Local Perspectives—0
MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings—0
MonoGRNet: A General Framework for Monocular 3D Object Detection—0
MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders—0
MonoMM: A Multi-scale Mamba-Enhanced Network for Real-time Monocular 3D Object Detection—0
MonoNext: A 3D Monocular Object Detection with ConvNext—0
MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts—0
MonoSOWA: Scalable monocular 3D Object detector Without human Annotations—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