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 101–125 of 1576 papers

TitleStatusHype
FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection—0
UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles—0
AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features—0
RadarNeXt: Real-Time and Reliable 3D Object Detector Based On 4D mmWave Imaging RadarCode1
V2X-DGPE: Addressing Domain Gaps and Pose Errors for Robust Collaborative 3D Object DetectionCode1
MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception—0
GBlobs: Explicit Local Structure via Gaussian Blobs for Improved Cross-Domain LiDAR-based 3D Object Detection—0
Leveraging Temporal Cues for Semi-Supervised Multi-View 3D Object Detection—0
GO-N3RDet: Geometry Optimized NeRF-enhanced 3D Object DetectorCode1
FSHNet: Fully Sparse Hybrid Network for 3D Object Detection—0
ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network—0
PillarHist: A Quantization-aware Pillar Feature Encoder based on Height-aware Histogram—0
Learning Class Prototypes for Unified Sparse-Supervised 3D Object Detection—0
V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection—0
CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes—0
TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning DistillationCode1
Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance-Level Spatial Refinement—0
HV-BEV: Decoupling Horizontal and Vertical Feature Sampling for Multi-View 3D Object DetectionCode0
TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object DetectionCode0
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object DetectionCode0
A New Adversarial Perspective for LiDAR-based 3D Object Detection—0
PromptDet: A Lightweight 3D Object Detection Framework with LiDAR Prompts—0
RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object DetectionCode1
RaCFormer: Towards High-Quality 3D Object Detection via Query-based Radar-Camera Fusion—0
HGSFusion: Radar-Camera Fusion with Hybrid Generation and Synchronization for 3D Object DetectionCode2
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All datasetsnuScenesScanNetV2SUN-RGBD valKITTI Cars EasyKITTI Cars HardnuScenes Camera OnlyKITTI Cars Easy valKITTI Cars Moderate valnuscenes Camera-RadarView-of-Delft (val)KITTI Cars Hard valDAIR-V2X-I

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