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
Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local GraphCode1
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDARCode1
CRN: Camera Radar Net for Accurate, Robust, Efficient 3D PerceptionCode1
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
FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object DetectionCode1
Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object DetectionCode1
FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D DetectionCode1
A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future DirectionsCode1
Frustum PointNets for 3D Object Detection from RGB-D DataCode1
Densely Constrained Depth Estimator for Monocular 3D Object DetectionCode1
A Fast Unified System for 3D Object Detection and TrackingCode1
Density-Insensitive Unsupervised Domain Adaption on 3D Object DetectionCode1
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object DetectionCode1
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object DetectionCode1
AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 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
From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object DetectionCode1
Fooling LiDAR Perception via Adversarial Trajectory PerturbationCode1
From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point DecoderCode1
Fully Sparse Fusion for 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
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