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 951–1000 of 1576 papers

TitleStatusHype
Cross-Domain Spatial Matching for Camera and Radar Sensor Data Fusion in Autonomous Vehicle Perception System—0
CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection—0
Cross-Modality 3D Object Detection—0
CTS: Sim-to-Real Unsupervised Domain Adaptation on 3D Detection—0
CubifAE-3D: Monocular Camera Space Cubification for Auto-Encoder based 3D Object Detection—0
Curiosity-driven 3D Object Detection Without Labels—0
CVFNet: Real-time 3D Object Detection by Learning Cross View Features—0
Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR—0
Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View—0
Introducing Depth into Transformer-based 3D Object Detection—0
DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception—0
DA-CIL: Towards Domain Adaptive Class-Incremental 3D Object Detection—0
DECADE: Towards Designing Efficient-yet-Accurate Distance Estimation Modules for Collision Avoidance in Mobile Advanced Driver Assistance Systems—0
Decoupled and Interactive Regression Modeling for High-performance One-stage 3D Object Detection—0
Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection—0
DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving—0
Deep Camera Pose Regression Using Pseudo-LiDAR—0
Deep Continuous Fusion for Multi-Sensor 3D Object Detection—0
Deep Fitting Degree Scoring Network for Monocular 3D Object Detection—0
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars—0
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection—0
Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey—0
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation—0
Deep Learning on Radar Centric 3D Object Detection—0
Deep Optics for Monocular Depth Estimation and 3D Object Detection—0
Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception—0
Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Data—0
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images—0
Delving into the Pre-training Paradigm of Monocular 3D Object Detection—0
Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?—0
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network—0
Dense Voxel Fusion for 3D Object Detection—0
Density-Based Clustering for 3D Object Detection in Point Clouds—0
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
Depth-aware Fusion Method based on Image and 4D Radar Spectrum for 3D Object Detection—0
Depth-discriminative Metric Learning for Monocular 3D Object Detection—0
DepthFusion: Depth-Aware Hybrid Feature Fusion for LiDAR-Camera 3D Object Detection—0
DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention—0
DetVPCC: RoI-based Point Cloud Sequence Compression for 3D Object Detection—0
Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection—0
DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection—0
DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation—0
Diffusion-based 3D Object Detection with Random Boxes—0
Digging Into Output Representation for Monocular 3D Object Detection—0
Dimension Embeddings for Monocular 3D Object Detection—0
Directed-CP: Directed Collaborative Perception for Connected and Autonomous Vehicles via Proactive Attention—0
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation—0
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment—0
DisARM: Displacement Aware Relation Module for 3D Detection—0
Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras—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