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 1301–1325 of 1576 papers

TitleStatusHype
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D ScenesCode1
Multi-Stage CNN-Based Monocular 3D Vehicle Localization and Orientation Estimation—0
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous VehiclesCode1
CenterFusion: Center-based Radar and Camera Fusion for 3D Object DetectionCode1
Disentangling 3D Prototypical Networks For Few-Shot Concept LearningCode1
Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises LossCode0
Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using FusionCode1
Multi-View Adaptive Fusion Network for 3D Object DetectionCode1
An Overview Of 3D Object Detection—0
Stereo Frustums: A Siamese Pipeline for 3D Object Detection—0
SF-UDA^3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object DetectionCode1
3D Object Detection and Pose Estimation of Unseen Objects in Color Images with Local Surface Embeddings—0
Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels—0
Monocular Differentiable Rendering for Self-Supervised 3D Object Detection—0
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous DrivingCode1
CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles—0
Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection—0
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection—0
Dynamic Edge Weights in Graph Neural Networks for 3D Object Detection—0
3D Object Detection and Tracking Based on Streaming Data—0
A Density-Aware PointRCNN for 3D Object Detection in Point Clouds—0
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach—0
RoIFusion: 3D Object Detection from LiDAR and Vision—0
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object DetectionCode1
RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image RepresentationCode1
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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