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 526–550 of 1576 papers

TitleStatusHype
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
Voxel R-CNN: Towards High Performance Voxel-based 3D Object DetectionCode1
RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous DrivingCode1
Exploring Data Augmentation for Multi-Modality 3D Object DetectionCode1
3D Object Detection with PointformerCode1
3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object DetectionCode1
Accurate 3D Object Detection using Energy-Based ModelsCode1
CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point CloudCode1
Monocular 3D Object Detection with Sequential Feature Association and Depth Hint AugmentationCode1
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D ScenesCode1
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
Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using FusionCode1
Multi-View Adaptive Fusion Network for 3D Object DetectionCode1
SF-UDA^3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object DetectionCode1
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous DrivingCode1
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object DetectionCode1
RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image RepresentationCode1
Deformable PV-RCNN: Improving 3D Object Detection with Learned DeformationsCode1
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionCode1
SSN: Shape Signature Networks for Multi-class Object Detection from Point CloudsCode1
Weakly Supervised 3D Object Detection from Point CloudsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MegFusionNDS0.77—Unverified
3MMFusion-eNDS0.77—Unverified
4DeepInteraction-largeNDS0.76—Unverified
5DeepInteraction-eNDS0.76—Unverified
6BEVFusion-eNDS0.76—Unverified
7RacoonPowerNDS0.76—Unverified
8ADS-TEAMNDS0.75—Unverified
9CenterPoint-FusionNDS0.75—Unverified
10UniTRNDS0.75—Unverified