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 351–375 of 1576 papers

TitleStatusHype
Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event CamerasCode1
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous DrivingCode1
Enhancing 3D Object Detection with 2D Detection-Guided Query AnchorsCode1
Deformable PV-RCNN: Improving 3D Object Detection with Learned DeformationsCode1
Delving into Localization Errors for Monocular 3D Object DetectionCode1
Delving into Motion-Aware Matching for Monocular 3D Object TrackingCode1
CRN: Camera Radar Net for Accurate, Robust, Efficient 3D PerceptionCode1
EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object DetectionCode1
End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and LocalizationCode1
A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future DirectionsCode1
End-to-End Pseudo-LiDAR for Image-Based 3D Object DetectionCode1
Densely Constrained Depth Estimator for Monocular 3D Object DetectionCode1
AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object DetectionCode1
Density-Insensitive Unsupervised Domain Adaption on 3D Object DetectionCode1
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object DetectionCode1
GO-N3RDet: Geometry Optimized NeRF-enhanced 3D Object DetectorCode1
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object DetectionCode1
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving ScenariosCode1
BEVNeXt: Reviving Dense BEV Frameworks for 3D Object DetectionCode1
Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain RobustnessCode1
CR3DT: Camera-RADAR Fusion for 3D Detection and TrackingCode1
MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object DetectionCode1
EPNet: Enhancing Point Features with Image Semantics for 3D Object DetectionCode1
SimDistill: Simulated Multi-modal Distillation for BEV 3D Object DetectionCode1
Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object DetectionCode1
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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