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 376–400 of 1576 papers

TitleStatusHype
EffiPerception: an Efficient Framework for Various Perception Tasks—0
GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection—0
Just Add $100 More: Augmenting NeRF-based Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem—0
V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions—0
SparseFusion: Efficient Sparse Multi-Modal Fusion Framework for Long-Range 3D Perception—0
SimPB: A Single Model for 2D and 3D Object Detection from Multiple CamerasCode1
RCooper: A Real-world Large-scale Dataset for Roadside Cooperative PerceptionCode2
PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest—0
Improving Distant 3D Object Detection Using 2D Box Supervision—0
CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow—0
MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation LearningCode2
Eliminating Cross-modal Conflicts in BEV Space for LiDAR-Camera 3D Object DetectionCode0
SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detection—0
Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D PerceptionCode1
LISO: Lidar-only Self-Supervised 3D Object DetectionCode2
SeSame: Simple, Easy 3D Object Detection with Point-Wise SemanticsCode1
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object DetectionCode1
Enhancing 3D Object Detection with 2D Detection-Guided Query AnchorsCode1
Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving—0
SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object DetectionCode2
RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR FeaturesCode1
LVIC: Multi-modality segmentation by Lifting Visual Info as Cue—0
ActFormer: Scalable Collaborative Perception via Active Queries—0
CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoors Object Detection from Multi-view ImagesCode1
CMDA: Cross-Modal and Domain Adversarial Adaptation for LiDAR-Based 3D Object Detection—0
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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