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 1426–1450 of 1576 papers

TitleStatusHype
SESS: Self-Ensembling Semi-Supervised 3D Object DetectionCode0
Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure SensorsCode0
Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Data—0
Towards Generalization Across Depth for Monocular 3D Object Detection—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
What You See is What You Get: Exploiting Visibility for 3D Object DetectionCode0
Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point CloudsCode0
Learning Depth-Guided Convolutions for Monocular 3D Object DetectionCode0
Relation Graph Network for 3D Object Detection in Point Clouds—0
PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement—0
PointPainting: Sequential Fusion for 3D Object DetectionCode1
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
Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving—0
Range Adaptation for 3D Object Detection in LiDAR—0
Task-Aware Monocular Depth Estimation for 3D Object DetectionCode0
Object-Centric Stereo Matching for 3D Object Detection—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