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 526–550 of 1576 papers

TitleStatusHype
Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care EnvironmentsCode0
Rotation Matters: Generalized Monocular 3D Object Detection for Various Camera Systems—0
Joint object detection and re-identification for 3D obstacle multi-camera systems—0
QE-BEV: Query Evolution for Bird's Eye View Object Detection in Varied ContextsCode0
Towards Long-Range 3D Object Detection for Autonomous Vehicles—0
WLST: Weak Labels Guided Self-training for Weakly-supervised Domain Adaptation on 3D Object DetectionCode1
CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object DetectionCode2
MagicDrive: Street View Generation with Diverse 3D Geometry ControlCode3
CoBEV: Elevating Roadside 3D Object Detection with Depth and Height ComplementarityCode1
Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training—0
Towards Robust 3D Object Detection In Rainy Conditions—0
Pixel-Aligned Recurrent Queries for Multi-View 3D Object Detection—0
LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis—0
MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings—0
Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature AugmentationCode1
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection—0
BEVHeight++: Toward Robust Visual Centric 3D Object Detection—0
M^2SODAI: Multi-Modal Maritime Object Detection Dataset With RGB and Hyperspectral Image Sensors—0
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge DistillationCode1
UniBEV: Multi-modal 3D Object Detection with Uniform BEV Encoders for Robustness against Missing Sensor ModalitiesCode1
Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object DatasetCode1
Robust 6DoF Pose Estimation Against Depth Noise and a Comprehensive Evaluation on a Mobile DatasetCode1
MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth CluesCode1
Unsupervised Domain Adaptation for Self-Driving from Past Traversal FeaturesCode0
FGFusion: Fine-Grained Lidar-Camera 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