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 1276–1300 of 1576 papers

TitleStatusHype
ePose: Let's Make EfficientPose More Generally ApplicableCode0
Pattern-Aware Data Augmentation for LiDAR 3D Object Detection—0
VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion—0
ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection—0
Joint stereo 3D object detection and implicit surface reconstructionCode0
Frustum Fusion: Pseudo-LiDAR and LiDAR Fusion for 3D Detection—0
VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection—0
Structure Information is the Key: Self-Attention RoI Feature Extractor in 3D Object Detection—0
Improved Pillar with Fine-grained Feature for 3D Object Detection—0
UrbanNet: Leveraging Urban Maps for Long Range 3D Object DetectionCode0
3D Object Detection Combining Semantic and Geometric Features from Point Clouds—0
3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation—0
MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation—0
Digging Into Output Representation for Monocular 3D Object Detection—0
Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object Detection—0
Automatic Map Update Using Dashcam Videos—0
Traffic-Net: 3D Traffic Monitoring Using a Single Camera—0
Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models—0
MBDF-Net: Multi-Branch Deep Fusion Network for 3D Object Detection—0
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network—0
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation—0
Semi-supervised 3D Object Detection via Adaptive Pseudo-Labeling—0
ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection—0
MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review—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