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 851–900 of 1576 papers

TitleStatusHype
aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range PerceptionCode1
ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection—0
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object DetectionCode1
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach—0
You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning—0
3D Cascade RCNN: High Quality Object Detection in Point CloudsCode1
Towards 3D Object Detection with 2D Supervision—0
PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection—0
Boosting Semi-Supervised 3D Object Detection with Semi-SamplingCode0
Robust Collaborative 3D Object Detection in Presence of Pose ErrorsCode1
Cross-Modality Knowledge Distillation Network for Monocular 3D Object DetectionCode1
Recursive Cross-View: Use Only 2D Detectors to Achieve 3D Object Detection without 3D Annotations—0
Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud LearningCode1
Hyperbolic Cosine Transformer for LiDAR 3D Object Detection—0
3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X OrchestrationCode0
PointSee: Image Enhances Point Cloud—0
Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection—0
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection—0
Multi-Camera Calibration Free BEV Representation for 3D Object Detection—0
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision—0
MSF3DDETR: Multi-Sensor Fusion 3D Detection Transformer for Autonomous Driving—0
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations—0
IDD-3D: Indian Driving Dataset for 3D Unstructured Road ScenesCode0
LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in TransformersCode0
XC: Exploring Quantitative Use Cases for Explanations in 3D Object DetectionCode0
PSA-Det3D: Pillar Set Abstraction for 3D object Detection—0
Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training—0
Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection—0
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel LogisticsCode1
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection—0
Bridging the Domain Gap for Multi-Agent PerceptionCode1
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining—0
SAILOR: Scaling Anchors via Insights into Latent Object RepresentationCode0
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds—0
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds—0
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point CloudsCode1
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object DetectionCode2
Bridged Transformer for Vision and Point Cloud 3D Object DetectionCode0
D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud SequenceCode1
PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object DetectionCode1
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment—0
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection—0
Out-of-Distribution Detection for LiDAR-based 3D Object Detection—0
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object DetectionCode1
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents—0
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence MapsCode2
Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects—0
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars—0
FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object DetectionCode0
BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal StereoCode2
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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