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 1451–1475 of 1576 papers

TitleStatusHype
A*3D Dataset: Towards Autonomous Driving in Challenging EnvironmentsCode0
F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds—0
Multi-Sensor 3D Object Box Refinement for Autonomous Driving—0
MLOD: A multi-view 3D object detection based on robust feature fusion method—0
Object Viewpoint Classification Based 3D Bounding Box Estimation for Autonomous Vehicles—0
3D Bounding Box Estimation for Autonomous Vehicles by Cascaded Geometric Constraints and Depurated 2D Detections Using 3D Results—0
StarNet: Targeted Computation for Object Detection in Point Clouds—0
Class-balanced Grouping and Sampling for Point Cloud 3D Object DetectionCode0
IoU Loss for 2D/3D Object DetectionCode0
Star-convex Polyhedra for 3D Object Detection and Segmentation in MicroscopyCode2
Fast Point R-CNN—0
Warp and Learn: Novel Views Generation for Vehicles and Other ObjectsCode0
STD: Sparse-to-Dense 3D Object Detector for Point Cloud—0
Improving 3D Object Detection for Pedestrians with Virtual Multi-View Synthesis Orientation Estimation—0
M3D-RPN: Monocular 3D Region Proposal Network for Object DetectionCode1
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation NetworkCode1
Point-Voxel CNN for Efficient 3D Deep LearningCode0
Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds—0
Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss—0
Attentional PointNet for 3D-Object Detection in Point CloudsCode0
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty EstimationCode1
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous DrivingCode0
VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view—0
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection—0
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts—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
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