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 401–425 of 1576 papers

TitleStatusHype
Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator—0
Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?Code0
False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy—0
FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird's-Eye View and Perspective View—0
Scalable Vision-Based 3D Object Detection and Monocular Depth Estimation for Autonomous DrivingCode1
Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample SelectionCode0
TUMTraf V2X Cooperative Perception DatasetCode4
Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust 3D Object DetectionCode1
Towards Unified 3D Object Detection via Algorithm and Data Unification—0
A Vanilla Multi-Task Framework for Dense Visual Prediction Solution to 1st VCL Challenge -- Multi-Task Robustness Track—0
AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene UnderstandingCode0
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode2
SDGE: Stereo Guided Depth Estimation for 360^ Camera Sets—0
LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object DetectionCode1
MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object DetectionCode2
AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision TransformerCode1
Neural Rendering based Urban Scene Reconstruction for Autonomous Driving—0
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration—0
Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private SourcesCode0
Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object DetectionCode2
ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object DetectionCode4
Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective—0
RIDERS: Radar-Infrared Depth Estimation for Robust SensingCode1
A Survey for Foundation Models in Autonomous Driving—0
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration—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