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 151–175 of 1576 papers

TitleStatusHype
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point CloudsCode2
MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation LearningCode2
MonoCD: Monocular 3D Object Detection with Complementary DepthsCode2
Monocular 3D Object Detection with Depth from MotionCode2
Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal FusionCode2
ARM3D: Attention-based relation module for indoor 3D object detectionCode1
ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D DataCode1
DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic FusionCode1
Deep Dive Into Gradients: Better Optimization for 3D Object Detection With Gradient-Corrected IoU SupervisionCode1
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous DrivingCode1
DCDet: Dynamic Cross-based 3D Object DetectorCode1
4D-Net for Learned Multi-Modal AlignmentCode1
DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object DetectionCode1
Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D SceneCode1
3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object DetectionCode1
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object DetectionCode1
Deep Hough Voting for 3D Object Detection in Point CloudsCode1
DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera ScenariosCode1
D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud SequenceCode1
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D DetectionCode1
Curricular Object Manipulation in LiDAR-based Object DetectionCode1
CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object DetectionCode1
DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within DatasetsCode1
Cross-Modality Knowledge Distillation Network for Monocular 3D Object DetectionCode1
3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance SegmentationCode1
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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