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 1451–1500 of 1576 papers

TitleStatusHype
Towards Generalization Across Depth for Monocular 3D Object Detection—0
Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Data—0
PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points—0
TANet: Robust 3D Object Detection from Point Clouds with Triple AttentionCode0
Learning Depth-Guided Convolutions for Monocular 3D Object DetectionCode0
Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point CloudsCode0
What You See is What You Get: Exploiting Visibility for 3D Object DetectionCode0
Relation Graph Network for 3D Object Detection in Point Clouds—0
PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement—0
RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving—0
Learning Permutation Invariant Representations using Memory NetworksCode0
PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module—0
End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds—0
Frustum VoxNet for 3D object detection from RGB-D or Depth images—0
Adaptive and Azimuth-Aware Fusion Network of Multimodal Local Features for 3D Object Detection—0
Patch Refinement -- Localized 3D Object Detection—0
Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving—0
epBRM: Improving a Quality of 3D Object Detection using End Point Box Regression Module—0
Range Adaptation for 3D Object Detection in LiDAR—0
Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving—0
Object-Centric Stereo Matching for 3D Object Detection—0
A*3D Dataset: Towards Autonomous Driving in Challenging EnvironmentsCode0
Task-Aware Monocular Depth Estimation for 3D Object DetectionCode0
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
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
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
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous DrivingCode0
Attentional PointNet for 3D-Object Detection in Point CloudsCode0
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection—0
VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view—0
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts—0
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D MappingCode0
Triangulation Learning Network: from Monocular to Stereo 3D Object DetectionCode0
Disentangling Monocular 3D Object Detection—0
Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints—0
RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud—0
Multimodal 3D Object Detection from Simulated PretrainingCode0
Monocular Plan View Networks for Autonomous Driving—0
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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