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 951–1000 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
Language-Assisted 3D Scene Understanding—0
Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving—0
Large receptive field strategy and important feature extraction strategy in 3D object detection—0
LASA: Instance Reconstruction from Real Scans using A Large-scale Aligned Shape Annotation Dataset—0
LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting—0
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving—0
LCV2I: Communication-Efficient and High-Performance Collaborative Perception Framework with Low-Resolution LiDAR—0
Learned Fusion: 3D Object Detection using Calibration-Free Transformer Feature Fusion—0
Learned Multimodal Compression for Autonomous Driving—0
Learning 3D Perception from Others' Predictions—0
Learning Class Prototypes for Unified Sparse-Supervised 3D Object Detection—0
Multi-View Representation is What You Need for Point-Cloud Pre-Training—0
Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels—0
Learning Temporal Cues by Predicting Objects Move for Multi-camera 3D Object Detection—0
Learning to Evaluate Perception Models Using Planner-Centric Metrics—0
Learning to Zoom and Unzoom—0
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection—0
Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection—0
Leveraging PointNet and PointNet++ for Lyft Point Cloud Classification Challenge—0
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation—0
Leveraging Temporal Cues for Semi-Supervised Multi-View 3D Object Detection—0
Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving—0
LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection—0
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations—0
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving—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