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 1351–1375 of 1576 papers

TitleStatusHype
Local Grid Rendering Networks for 3D Object Detection in Point Clouds—0
Optimisation of the PointPillars network for 3D object detection in point clouds—0
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey—0
Expandable YOLO: 3D Object Detection from RGB-D Images—0
AFDet: Anchor Free One Stage 3D Object DetectionCode1
Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time—0
Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles—0
Generative Sparse Detection Networks for 3D Single-shot Object DetectionCode2
Center-based 3D Object Detection and TrackingCode2
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies—0
H3DNet: 3D Object Detection Using Hybrid Geometric PrimitivesCode1
Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection—0
Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection—0
CubifAE-3D: Monocular Camera Space Cubification for Auto-Encoder based 3D Object Detection—0
SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds—0
3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance SegmentationCode1
A Hierarchical Graph Network for 3D Object Detection on Point Clouds—0
Density-Based Clustering for 3D Object Detection in Point Clouds—0
Structure Aware Single-Stage 3D Object Detection From Point CloudCode1
IDA-3D: Instance-Depth-Aware 3D Object Detection From Stereo Vision for Autonomous DrivingCode1
Single-Shot Monocular RGB-D Imaging Using Uneven Double Refraction—0
BidNet: Binocular Image Dehazing Without Explicit Disparity EstimationCode0
Joint 3D Instance Segmentation and Object Detection for Autonomous Driving—0
Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding—0
RV-FuseNet: Range View Based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting—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