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 301–325 of 1576 papers

TitleStatusHype
Geometry Uncertainty Projection Network for Monocular 3D Object DetectionCode1
Group-Free 3D Object Detection via TransformersCode1
HVDetFusion: A Simple and Robust Camera-Radar Fusion FrameworkCode1
From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point DecoderCode1
From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object DetectionCode1
FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D DetectionCode1
CubeSLAM: Monocular 3D Object SLAMCode1
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse WeatherCode1
Fooling LiDAR Perception via Adversarial Trajectory PerturbationCode1
Frustum PointNets for 3D Object Detection from RGB-D DataCode1
CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object DetectionCode1
3D Object Detection from Images for Autonomous Driving: A SurveyCode1
Cross-Modality Knowledge Distillation Network for Monocular 3D Object DetectionCode1
AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision TransformerCode1
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object DetectionCode1
Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode1
Curricular Object Manipulation in LiDAR-based Object DetectionCode1
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label EnhancementCode1
FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle DetectionCode1
A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial AttacksCode1
Back-tracing Representative Points for Voting-based 3D Object Detection in Point CloudsCode1
Finding Your (3D) Center: 3D Object Detection Using a Learned LossCode1
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object DetectionCode1
AutoShape: Real-Time Shape-Aware Monocular 3D Object DetectionCode1
FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object DetectionCode1
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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