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 626–650 of 1576 papers

TitleStatusHype
Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection—0
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data—0
Explainable Multi-Camera 3D Object Detection with Transformer-Based Saliency Maps—0
Expandable YOLO: 3D Object Detection from RGB-D Images—0
Cirrus: A Long-range Bi-pattern LiDAR Dataset—0
EVT: Efficient View Transformation for Multi-Modal 3D Object Detection—0
Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization—0
Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training—0
Evaluating the Impact of Synthetic Data on Object Detection Tasks in Autonomous Driving—0
CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar—0
Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection—0
A Point-Based Approach to Efficient LiDAR Multi-Task Perception—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
Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects—0
AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation—0
Anyview: Generalizable Indoor 3D Object Detection with Variable Frames—0
Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding—0
Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding—0
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection—0
Multi-View Representation is What You Need for Point-Cloud Pre-Training—0
Enhancing 3D Object Detection in Autonomous Vehicles Based on Synthetic Virtual Environment Analysis—0
Enhanced K-Radar: Optimal Density Reduction to Improve Detection Performance and Accessibility of 4D Radar Tensor-based Object Detection—0
CatFree3D: Category-agnostic 3D Object Detection with Diffusion—0
Anytime-Lidar: Deadline-aware 3D Object Detection—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