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
BEVFusion: A Simple and Robust LiDAR-Camera Fusion FrameworkCode2
CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object DetectionCode2
MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object DetectionCode2
CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse TransformersCode2
SoftGroup for 3D Instance Segmentation on Point CloudsCode2
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
DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward GuidanceCode1
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous DrivingCode1
3D Small Object Detection with Dynamic Spatial PruningCode1
4D-Net for Learned Multi-Modal AlignmentCode1
DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against CorruptionsCode1
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
DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D DetectorsCode1
DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera ScenariosCode1
DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and TrackingCode1
Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise BinarizationCode1
DQS3D: Densely-matched Quantization-aware Semi-supervised 3D DetectionCode1
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge DistillationCode1
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D DetectionCode1
DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object DetectionCode1
Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object DetectionCode1
DiffuBox: Refining 3D Object Detection with Point DiffusionCode1
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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