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 1026–1050 of 1576 papers

TitleStatusHype
M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation—0
DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D DetectorsCode1
RBGNet: Ray-based Grouping for 3D Object DetectionCode1
ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression FrameworkCode1
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded ScenesCode1
Homography Loss for Monocular 3D Object DetectionCode1
Multi-Class Road User Detection With 3+1D Radar in the View-of-Delft DatasetCode2
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection—0
Accelerating Federated Edge Learning via Topology Optimization—0
ImpDet: Exploring Implicit Fields for 3D Object Detection—0
A Survey of Robust 3D Object Detection Methods in Point Clouds—0
Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation—0
BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object DetectionCode3
BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal TransformersCode4
Fusing Event-based and RGB camera for Robust Object Detection in Adverse ConditionsCode1
Image-to-Lidar Self-Supervised Distillation for Autonomous Driving DataCode2
LiDAR Snowfall Simulation for Robust 3D Object DetectionCode2
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object DetectionCode1
Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task—0
Point2Seq: Detecting 3D Objects as SequencesCode1
EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose EstimationCode3
MonoDETR: Depth-guided Transformer for Monocular 3D Object DetectionCode2
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D PerceptionCode1
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with TransformersCode2
Stereo Neural Vernier CaliperCode1
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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