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 951–975 of 1576 papers

TitleStatusHype
epBRM: Improving a Quality of 3D Object Detection using End Point Box Regression Module—0
Improving Distant 3D Object Detection Using 2D Box Supervision—0
Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization—0
Improving Online Lane Graph Extraction by Object-Lane Clustering—0
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection—0
Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective—0
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining—0
InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling—0
InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors—0
In search of inliers: 3d correspondence by local and global voting—0
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning—0
InstanceBEV: Unifying Instance and BEV Representation for Global Modeling—0
Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filtering—0
Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention—0
IPOD: Intensive Point-based Object Detector for Point Cloud—0
IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain—0
Is a 3D-Tokenized LLM the Key to Reliable Autonomous Driving?—0
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions—0
It's All Around You: Range-Guided Cylindrical Network for 3D Object Detection—0
Joint 3D Instance Segmentation and Object Detection for Autonomous Driving—0
Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds—0
Joint object detection and re-identification for 3D obstacle multi-camera systems—0
Just Add $100 More: Augmenting NeRF-based Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem—0
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection—0
Label-Efficient 3D Object Detection For Road-Side Units—0
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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