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 1476–1500 of 1576 papers

TitleStatusHype
OpenNav: Efficient Open Vocabulary 3D Object Detection for Smart Wheelchair NavigationCode0
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object DetectionCode0
Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure SensorsCode0
TR3D: Towards Real-Time Indoor 3D Object DetectionCode0
OKGR: Occluded Keypoint Generation and Refinement for 3D Object DetectionCode0
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose EstimationCode0
ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object DetectionCode0
Attentional PointNet for 3D-Object Detection in Point CloudsCode0
Computer Vision Aided mmWave Beam Alignment in V2X CommunicationsCode0
OBMO: One Bounding Box Multiple Objects for Monocular 3D Object DetectionCode0
ePose: Let's Make EfficientPose More Generally ApplicableCode0
AShapeFormer: Semantics-Guided Object-Level Active Shape Encoding for 3D Object Detection via TransformersCode0
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance VotingCode0
Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?Code0
Multi-View 3D Object Detection Network for Autonomous DrivingCode0
Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithmsCode0
3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X OrchestrationCode0
Multi-V2X: A Large Scale Multi-modal Multi-penetration-rate Dataset for Cooperative PerceptionCode0
Multimodal 3D Object Detection from Simulated PretrainingCode0
MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object DetectionCode0
MR3D-Net: Dynamic Multi-Resolution 3D Sparse Voxel Grid Fusion for LiDAR-Based Collective PerceptionCode0
Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systemsCode0
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object DetectionCode0
MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object LocalizationCode0
Transformer-Based Sensor Fusion for Autonomous Driving: A SurveyCode0
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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