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 901–950 of 1576 papers

TitleStatusHype
Boosting 3D Object Detection with Semantic-Aware Multi-Branch Framework—0
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey—0
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision—0
Boosting Single-Frame 3D Object Detection by Simulating Multi-Frame Point Clouds—0
Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds—0
BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization—0
Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection—0
Bridging the View Disparity Between Radar and Camera Features for Multi-modal Fusion 3D Object Detection—0
BugNIST -- a Large Volumetric Dataset for Object Detection under Domain Shift—0
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception—0
Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection—0
Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving—0
CARLA Drone: Monocular 3D Object Detection from a Different Perspective—0
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection—0
CatFree3D: Category-agnostic 3D Object Detection with Diffusion—0
Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding—0
Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects—0
CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar—0
Cirrus: A Long-range Bi-pattern LiDAR Dataset—0
Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?—0
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation—0
CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow—0
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts—0
Clusterformer: Cluster-based Transformer for 3D Object Detection in Point Clouds—0
ClusterFusion: Leveraging Radar Spatial Features for Radar-Camera 3D Object Detection in Autonomous Vehicles—0
CMDA: Cross-Modal and Domain Adversarial Adaptation for LiDAR-Based 3D Object Detection—0
CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection—0
CoBEVFusion: Cooperative Perception with LiDAR-Camera Bird's-Eye View Fusion—0
Cocoon: Robust Multi-Modal Perception with Uncertainty-Aware Sensor Fusion—0
CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving—0
CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles—0
Collaborative 3D Object Detection for Automatic Vehicle Systems via Learnable Communications—0
Collective Perception Datasets for Autonomous Driving: A Comprehensive Review—0
Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques—0
Complete 3d relationships extraction modality alignment network for 3d dense captioning—0
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds—0
Confidence Guided Stereo 3D Object Detection with Split Depth Estimation—0
Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection—0
Context-Aware Data Augmentation for LIDAR 3D Object Detection—0
ContextualFusion: Context-Based Multi-Sensor Fusion for 3D Object Detection in Adverse Operating Conditions—0
ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection—0
Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds—0
CornerPoint3D: Look at the Nearest Corner Instead of the Center—0
CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes—0
Cost-Aware Evaluation and Model Scaling for LiDAR-Based 3D Object Detection—0
CP-VoteNet: Contrastive Prototypical VoteNet for Few-Shot Point Cloud Object Detection—0
CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer—0
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection—0
CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation—0
Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving—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