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 326–350 of 1576 papers

TitleStatusHype
Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor LocalizationCode1
LSSInst: Improving Geometric Modeling in LSS-Based BEV Perception with Instance RepresentationCode1
DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within DatasetsCode1
M3DSSD: Monocular 3D Single Stage Object DetectorCode1
DCDet: Dynamic Cross-based 3D Object DetectorCode1
DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object DetectionCode1
Benchmarking Robustness of 3D Object Detection to Common CorruptionsCode1
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object DetectionCode1
FASTer: Focal Token Acquiring-and-Scaling Transformer for Long-term 3D Object DetectionCode1
CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object DetectionCode1
AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR DataCode1
3D Object Detection from Images for Autonomous Driving: A SurveyCode1
FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle DetectionCode1
Cross-Modality Knowledge Distillation Network for Monocular 3D Object DetectionCode1
End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and LocalizationCode1
LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object DetectionCode1
Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban EnvironmentsCode1
A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial AttacksCode1
LinK: Linear Kernel for LiDAR-based 3D PerceptionCode1
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object DetectionCode1
AutoShape: Real-Time Shape-Aware Monocular 3D Object DetectionCode1
End-to-End Pseudo-LiDAR for Image-Based 3D Object DetectionCode1
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object DetectionCode1
CRN: Camera Radar Net for Accurate, Robust, Efficient 3D PerceptionCode1
EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye ViewCode1
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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