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 426–450 of 1576 papers

TitleStatusHype
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object DetectionCode2
SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object DetectionCode1
MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object DetectionCode2
You Only Look Bottom-Up for Monocular 3D Object Detection—0
Pragmatic Communication in Multi-Agent Collaborative PerceptionCode0
Large receptive field strategy and important feature extraction strategy in 3D object detection—0
MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection—0
CascadeV-Det: Cascade Point Voting for 3D Object DetectionCode0
DCDet: Dynamic Cross-based 3D Object DetectorCode1
DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception—0
3D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Data—0
Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook—0
Dual-Perspective Knowledge Enrichment for Semi-Supervised 3D Object DetectionCode0
SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labelling—0
RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAMCode2
WidthFormer: Toward Efficient Transformer-based BEV View TransformationCode2
VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection—0
Distilling Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection—0
Depth-discriminative Metric Learning for Monocular 3D Object Detection—0
CaKDP: Category-aware Knowledge Distillation and Pruning Framework for Lightweight 3D Object DetectionCode1
Multi-agent Collaborative Perception via Motion-aware Robust Communication Network—0
CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoor Object Detection from Multi-view ImagesCode1
Prompt3D: Random Prompt Assisted Weakly-Supervised 3D Object DetectionCode0
HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely-Supervised 3D Object DetectionCode1
Towards Robust 3D Object Detection with LiDAR and 4D Radar Fusion in Various Weather ConditionsCode1
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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