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 1051–1075 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
MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes—0
MoRAL: Motion-aware Multi-Frame 4D Radar and LiDAR Fusion for Robust 3D Object Detection—0
MOSE: Boosting Vision-based Roadside 3D Object Detection with Scene Cues—0
MS23D: A 3D Object Detection Method Using Multi-Scale Semantic Feature Points to Construct 3D Feature Layer—0
MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception—0
MSF3DDETR: Multi-Sensor Fusion 3D Detection Transformer for Autonomous Driving—0
MSSF: A 4D Radar and Camera Fusion Framework With Multi-Stage Sampling for 3D Object Detection in Autonomous Driving—0
MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection—0
MT-Net Submission to the Waymo 3D Detection Leaderboard—0
Multi-agent Collaborative Perception via Motion-aware Robust Communication Network—0
Multi-Camera Calibration Free BEV Representation for 3D Object Detection—0
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach—0
Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles—0
Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery—0
Multi-Class 3D Object Detection with Single-Class Supervision—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