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
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
ViewFormer: Exploring Spatiotemporal Modeling for Multi-View 3D Occupancy Perception via View-Guided TransformersCode2
BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection—0
PVTransformer: Point-to-Voxel Transformer for Scalable 3D Object Detection—0
Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object DetectionCode0
Leveraging PointNet and PointNet++ for Lyft Point Cloud Classification Challenge—0
Reliable Student: Addressing Noise in Semi-Supervised 3D Object DetectionCode1
Cross-Domain Spatial Matching for Camera and Radar Sensor Data Fusion in Autonomous Vehicle Perception System—0
Commonsense Prototype for Outdoor Unsupervised 3D Object DetectionCode2
Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object DetectionCode1
ContextualFusion: Context-Based Multi-Sensor Fusion for 3D Object Detection in Adverse Operating Conditions—0
NeRF-DetS: Enhanced Adaptive Spatial-wise Sampling and View-wise Fusion Strategies for NeRF-based Indoor Multi-view 3D Object Detection—0
Language-Driven Active Learning for Diverse Open-Set 3D Object DetectionCode0
A Point-Based Approach to Efficient LiDAR Multi-Task Perception—0
Multimodal 3D Object Detection on Unseen Domains—0
TempBEV: Improving Learned BEV Encoders with Combined Image and BEV Space Temporal Aggregation—0
Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection—0
Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection—0
VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection—0
Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patterns—0
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving—0
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong ElicitingCode2
Sparse Points to Dense Clouds: Enhancing 3D Detection with Limited LiDAR Data—0
Label-Efficient 3D Object Detection For Road-Side Units—0
Better Monocular 3D Detectors with LiDAR from the PastCode1
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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