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 426–450 of 1576 papers

TitleStatusHype
3D Small Object Detection with Dynamic Spatial PruningCode1
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object DetectionCode1
GO-N3RDet: Geometry Optimized NeRF-enhanced 3D Object DetectorCode1
DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward GuidanceCode1
DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic FusionCode1
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation ModelsCode1
Physical Attack on Monocular Depth Estimation with Optimal Adversarial PatchesCode1
3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance SegmentationCode1
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous DrivingCode1
3D Object Detection for Autonomous Driving: A SurveyCode1
GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point CloudsCode1
An End-to-End Transformer Model for 3D Object DetectionCode1
Ground-aware Monocular 3D Object Detection for Autonomous DrivingCode1
HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point CloudsCode1
HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object DetectionCode1
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point CloudsCode1
DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera ScenariosCode1
CaKDP: Category-aware Knowledge Distillation and Pruning Framework for Lightweight 3D Object DetectionCode1
Point Cloud Pre-Training With Natural 3D StructuresCode1
Learning Auxiliary Monocular Contexts Helps Monocular 3D Object DetectionCode1
EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye ViewCode1
GeoBEV: Learning Geometric BEV Representation for Multi-view 3D Object DetectionCode1
Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object DetectionCode1
Canadian Adverse Driving Conditions DatasetCode1
General Geometry-aware Weakly Supervised 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