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 1251–1275 of 1576 papers

TitleStatusHype
MapFusion: A General Framework for 3D Object Detection with HDMaps—0
ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object DetectionCode1
Offboard 3D Object Detection from Point Cloud Sequences—0
Machine-learning based methodologies for 3d x-ray measurement, characterization and optimization for buried structures in advanced ic packages—0
A Simple and Efficient Multi-task Network for 3D Object Detection and Road UnderstandingCode1
Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection—0
labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point CloudsCode1
IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image—0
Pseudo-labeling for Scalable 3D Object Detection—0
Categorical Depth Distribution Network for Monocular 3D Object DetectionCode1
3D Object Detection and Instance Segmentation from 3D Range and 2D Color Images—0
DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds—0
Object Removal Attacks on LiDAR-based 3D Object Detectors—0
Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues—0
Ground-aware Monocular 3D Object Detection for Autonomous DrivingCode1
PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection—0
Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models—0
A two-stage data association approach for 3D Multi-object Tracking—0
Auto4D: Learning to Label 4D Objects from Sequential Point Clouds—0
PLUMENet: Efficient 3D Object Detection from Stereo ImagesCode1
SA-Det3D: Self-Attention Based Context-Aware 3D Object DetectionCode1
RangeDet: In Defense of Range View for LiDAR-Based 3D Object DetectionCode1
VENet: Voting Enhancement Network for 3D Object Detection—0
MLVSNet: Multi-Level Voting Siamese Network for 3D Visual TrackingCode0
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 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