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 626–650 of 1576 papers

TitleStatusHype
Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object DetectionCode1
GMM: Delving into Gradient Aware and Model Perceive Depth Mining for Monocular 3D Detection—0
Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithmsCode0
Tame a Wild Camera: In-the-Wild Monocular Camera CalibrationCode1
Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection—0
Frame Fusion with Vehicle Motion Prediction for 3D Object Detection—0
Predict to Detect: Prediction-guided 3D Object Detection using Sequential ImagesCode1
Towards a Robust Sensor Fusion Step for 3D Object Detection on Corrupted DataCode0
Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine PerceptionCode2
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point CloudsCode2
Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization—0
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio MaskingCode0
Weakly Supervised 3D Object Detection with Multi-Stage Generalization—0
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud SequencesCode1
Multi-View Representation is What You Need for Point-Cloud Pre-Training—0
SAM3D: Zero-Shot 3D Object Detection via Segment Anything ModelCode2
OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection—0
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception—0
Doubly Robust Self-TrainingCode0
Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color ContrastCode1
UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous DrivingCode2
VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D AnnotationsCode0
Monocular 2D Camera-based Proximity Monitoring for Human-Machine Collision Warning on Construction SitesCode0
View-to-Label: Multi-View Consistency for Self-Supervised 3D Object Detection—0
Radar Enlighten the Dark: Enhancing Low-Visibility Perception for Automated Vehicles with Camera-Radar FusionCode1
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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