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 1026–1050 of 1576 papers

TitleStatusHype
Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection—0
Towards a Robust Sensor Fusion Step for 3D Object Detection on Corrupted DataCode0
Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization—0
Weakly Supervised 3D Object Detection with Multi-Stage Generalization—0
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio MaskingCode0
Multi-View Representation is What You Need for Point-Cloud Pre-Training—0
OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection—0
Doubly Robust Self-TrainingCode0
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception—0
View-to-Label: Multi-View Consistency for Self-Supervised 3D Object Detection—0
Monocular 2D Camera-based Proximity Monitoring for Human-Machine Collision Warning on Construction SitesCode0
VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D AnnotationsCode0
Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving—0
RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion—0
DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object Detection—0
RCFusion: Fusing 4-D Radar and Camera With Bird’s-Eye View Features for 3-D Object Detection—0
Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection—0
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer—0
MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes—0
SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving—0
TransCAR: Transformer-based Camera-And-Radar Fusion for 3D Object Detection—0
InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors—0
HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score FilteringCode0
Gradient-based Maximally Interfered Retrieval for Domain Incremental 3D Object DetectionCode0
OriCon3D: Effective 3D Object Detection using Orientation and Confidence—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