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 476–500 of 1576 papers

TitleStatusHype
Object as Query: Lifting any 2D Object Detector to 3D DetectionCode1
Frustum PointNets for 3D Object Detection from RGB-D DataCode1
FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object DetectionCode1
FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D DetectionCode1
Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object DetectionCode1
Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event CamerasCode1
Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous VehiclesCode1
ConQueR: Query Contrast Voxel-DETR for 3D Object DetectionCode1
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR DataCode1
CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point CloudCode1
Attention-based Proposals Refinement for 3D Object DetectionCode1
From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object DetectionCode1
From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point DecoderCode1
4D-Net for Learned Multi-Modal AlignmentCode1
One Million Scenes for Autonomous Driving: ONCE DatasetCode1
Fully Sparse Fusion for 3D Object DetectionCode1
Open-Vocabulary Point-Cloud Object Detection without 3D AnnotationCode1
PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object DetectionCode1
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse WeatherCode1
Pillar-based Object Detection for Autonomous DrivingCode1
Fooling LiDAR Perception via Adversarial Trajectory PerturbationCode1
Lightweight LiDAR-Camera 3D Dynamic Object Detection and Multi-Class Trajectory PredictionCode1
PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDARCode1
Color-aware two-branch DCNN for efficient plant disease classificationCode1
Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MegFusionNDS0.77—Unverified
3MMFusion-eNDS0.77—Unverified
4BEVFusion-eNDS0.76—Unverified
5RacoonPowerNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8FusionVPENDS0.75—Unverified
9FocalFormer3D-FNDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified