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 1101–1150 of 1576 papers

TitleStatusHype
Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection—0
Embracing Single Stride 3D Object Detector with Sparse Transformer—0
Learning Auxiliary Monocular Contexts Helps Monocular 3D Object DetectionCode1
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection—0
Real-time Collision Risk Estimation based on Stochastic Reachability SpacesCode0
SoK: Vehicle Orientation Representations for Deep Rotation EstimationCode1
Behind the Curtain: Learning Occluded Shapes for 3D Object DetectionCode1
SGM3D: Stereo Guided Monocular 3D Object DetectionCode1
Open-set 3D Object Detection—0
Scalable Primitives for Generalized Sensor Fusion in Autonomous Vehicles—0
FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection—0
Pattern-Aware Data Augmentation for LiDAR 3D Object Detection—0
Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object DetectionCode1
ePose: Let's Make EfficientPose More Generally ApplicableCode0
VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion—0
ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection—0
Joint stereo 3D object detection and implicit surface reconstructionCode0
BoxeR: Box-Attention for 2D and 3D TransformersCode1
Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Point Density Level EstimationCode1
ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D DataCode1
Multimodal Virtual Point 3D DetectionCode1
Frustum Fusion: Pseudo-LiDAR and LiDAR Fusion for 3D Detection—0
Learning Distilled Collaboration Graph for Multi-Agent PerceptionCode1
Structure Information is the Key: Self-Attention RoI Feature Extractor in 3D Object Detection—0
VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection—0
Object DGCNN: 3D Object Detection using Dynamic GraphsCode1
DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D QueriesCode1
Improved Pillar with Fine-grained Feature for 3D Object Detection—0
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDARCode1
UrbanNet: Leveraging Urban Maps for Long Range 3D Object DetectionCode0
3D Object Detection Combining Semantic and Geometric Features from Point Clouds—0
3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation—0
MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation—0
Digging Into Output Representation for Monocular 3D Object Detection—0
Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object Detection—0
Automatic Map Update Using Dashcam Videos—0
MVM3Det: A Novel Method for Multi-view Monocular 3D DetectionCode1
Advancing Self-supervised Monocular Depth Learning with Sparse LiDARCode1
Traffic-Net: 3D Traffic Monitoring Using a Single Camera—0
An End-to-End Transformer Model for 3D Object DetectionCode1
OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle CommunicationCode1
Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models—0
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object DetectionCode1
Voxel Transformer for 3D Object DetectionCode1
4D-Net for Learned Multi-Modal AlignmentCode1
Spatio-temporal Self-Supervised Representation Learning for 3D Point CloudsCode1
MBDF-Net: Multi-Branch Deep Fusion Network for 3D Object Detection—0
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network—0
AutoShape: Real-Time Shape-Aware Monocular 3D Object DetectionCode1
Improving 3D Object Detection with Channel-wise TransformerCode1
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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
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