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 401–425 of 1576 papers

TitleStatusHype
3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection TransformersCode1
Multimodal Transformer for Automatic 3D Annotation and Object DetectionCode1
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty EstimationCode1
DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object DetectionCode1
Multi-View Adaptive Fusion Network for 3D Object DetectionCode1
Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise BinarizationCode1
CORE: Cooperative Reconstruction for Multi-Agent PerceptionCode1
General Geometry-aware Weakly Supervised 3D Object DetectionCode1
Bootstraping Clustering of Gaussians for View-consistent 3D Scene UnderstandingCode1
DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic FusionCode1
BADet: Boundary-Aware 3D Object Detection from Point CloudsCode1
Object as Query: Lifting any 2D Object Detector to 3D DetectionCode1
3D Object Detection for Autonomous Driving: A SurveyCode1
Among Us: Adversarially Robust Collaborative Perception by ConsensusCode1
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous VehiclesCode1
HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point CloudsCode1
ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image VotesCode1
FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object DetectionCode1
Fully Sparse Fusion for 3D Object DetectionCode1
3DRM:Pair-wise relation module for 3D object detectionCode1
BoxeR: Box-Attention for 2D and 3D TransformersCode1
Frustum PointNets for 3D Object Detection from RGB-D DataCode1
Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object DetectionCode1
DSGN: Deep Stereo Geometry Network for 3D Object DetectionCode1
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDARCode1
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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