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 751–775 of 1576 papers

TitleStatusHype
Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection—0
MCBLT: Multi-Camera Multi-Object 3D Tracking in Long Videos—0
LIFT: Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection—0
AGO-Net: Association-Guided 3D Point Cloud Object Detection Network—0
A General Pipeline for 3D Detection of Vehicles—0
Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss—0
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers—0
DetVPCC: RoI-based Point Cloud Sequence Compression for 3D Object Detection—0
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds—0
LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network—0
LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception—0
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection—0
DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention—0
BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo—0
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach—0
LiDAR-in-the-Loop Hyperparameter Optimization—0
3D Object Detection With Latent Support Surfaces—0
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving—0
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks—0
A Flexible Multi-view Multi-modal Imaging System for Outdoor Scenes—0
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations—0
LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection—0
3DifFusionDet: Diffusion Model for 3D Object Detection with Robust LiDAR-Camera Fusion—0
MonoCoP: Chain-of-Prediction for Monocular 3D Object Detection—0
Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MegFusionNDS0.77—Unverified
3MMFusion-eNDS0.77—Unverified
4BEVFusion-eNDS0.76—Unverified
5RacoonPowerNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8FusionVPENDS0.75—Unverified
9FocalFormer3D-FNDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified