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 1201–1250 of 1576 papers

TitleStatusHype
MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation—0
Methodology for a Statistical Analysis of Influencing Factors on 3D Object Detection Performance—0
Minkowski Tracker: A Sparse Spatio-Temporal R-CNN for Joint Object Detection and Tracking—0
Mitigating Perspective Distortion-induced Shape Ambiguity in Image Crops—0
MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection—0
MLOD: A multi-view 3D object detection based on robust feature fusion method—0
MMDR: A Result Feature Fusion Object Detection Approach for Autonomous System—0
mmFUSION: Multimodal Fusion for 3D Objects Detection—0
MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review—0
Hierarchical Point Attention for Indoor 3D Object Detection—0
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation—0
MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer—0
MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation—0
MonoCoP: Chain-of-Prediction for Monocular 3D Object Detection—0
MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models—0
Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss—0
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach—0
Monocular 3D Object Detection for Autonomous Driving—0
3D Object Detection from a Single Fisheye Image Without a Single Fisheye Training Image—0
Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference—0
Monocular 3D Object Detection via Feature Domain Adaptation—0
Monocular 3D Object Detection via Geometric Reasoning on Keypoints—0
Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver—0
Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation—0
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning—0
Monocular Differentiable Rendering for Self-Supervised 3D Object Detection—0
Monocular Plan View Networks for Autonomous Driving—0
MonoDETRNext: Next-Generation Accurate and Efficient Monocular 3D Object Detector—0
MonoDiff: Monocular 3D Object Detection and Pose Estimation with Diffusion Models—0
MonoEdge: Monocular 3D Object Detection Using Local Perspectives—0
MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings—0
MonoGRNet: A General Framework for Monocular 3D Object Detection—0
MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders—0
MonoMM: A Multi-scale Mamba-Enhanced Network for Real-time Monocular 3D Object Detection—0
MonoNext: A 3D Monocular Object Detection with ConvNext—0
MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts—0
MonoSOWA: Scalable monocular 3D Object detector Without human Annotations—0
MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes—0
MoRAL: Motion-aware Multi-Frame 4D Radar and LiDAR Fusion for Robust 3D Object Detection—0
MOSE: Boosting Vision-based Roadside 3D Object Detection with Scene Cues—0
MS23D: A 3D Object Detection Method Using Multi-Scale Semantic Feature Points to Construct 3D Feature Layer—0
MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception—0
MSF3DDETR: Multi-Sensor Fusion 3D Detection Transformer for Autonomous Driving—0
MSSF: A 4D Radar and Camera Fusion Framework With Multi-Stage Sampling for 3D Object Detection in Autonomous Driving—0
MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection—0
MT-Net Submission to the Waymo 3D Detection Leaderboard—0
Multi-agent Collaborative Perception via Motion-aware Robust Communication Network—0
Multi-Camera Calibration Free BEV Representation for 3D Object Detection—0
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach—0
Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles—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