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 576–600 of 1576 papers

TitleStatusHype
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey—0
Delving into Motion-Aware Matching for Monocular 3D Object TrackingCode1
UniM^2AE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous DrivingCode1
QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection—0
Representation Disparity-aware Distillation for 3D Object Detection—0
ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions—0
DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within DatasetsCode1
SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera VideosCode2
Far3D: Expanding the Horizon for Surround-view 3D Object DetectionCode1
MonoNeRD: NeRF-like Representations for Monocular 3D Object DetectionCode1
ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object DetectionCode1
GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point CloudsCode1
UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View RepresentationCode2
PatchContrast: Self-Supervised Pre-training for 3D Object Detection—0
UniWorld: Autonomous Driving Pre-training via World ModelsCode0
PV-SSD: A Multi-Modal Point Cloud Feature Fusion Method for Projection Features and Variable Receptive Field Voxel Features—0
MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaption in 3D Object DetectionCode1
Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D Radar—0
An Empirical Analysis of Range for 3D Object Detection—0
V-DETR: DETR with Vertex Relative Position Encoding for 3D Object DetectionCode1
FocalFormer3D : Focusing on Hard Instance for 3D Object DetectionCode2
PARTNER: Level up the Polar Representation for LiDAR 3D Object DetectionCode1
FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels—0
QUEST: Query Stream for Practical Cooperative PerceptionCode0
MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection GeneralizationCode0
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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