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 526–550 of 1576 papers

TitleStatusHype
Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous DrivingCode1
Fully Sparse Fusion for 3D Object DetectionCode1
Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode1
IDA-3D: Instance-Depth-Aware 3D Object Detection From Stereo Vision for Autonomous DrivingCode1
ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image VotesCode1
CoCoNets: Continuous Contrastive 3D Scene RepresentationsCode1
HoughNet: Integrating near and long-range evidence for visual detectionCode1
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object DetectionCode1
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse WeatherCode1
A Simple Baseline for Multi-Camera 3D Object DetectionCode1
HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object DetectionCode1
Co-Fix3D: Enhancing 3D Object Detection with Collaborative RefinementCode1
Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban EnvironmentsCode1
Finding Your (3D) Center: 3D Object Detection Using a Learned LossCode1
From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point DecoderCode1
Is Pseudo-Lidar needed for Monocular 3D Object detection?Code1
FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D DetectionCode1
CoBEV: Elevating Roadside 3D Object Detection with Depth and Height ComplementarityCode1
FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle DetectionCode1
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDARCode1
labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point CloudsCode1
FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object DetectionCode1
Color-aware two-branch DCNN for efficient plant disease classificationCode1
Accelerate 3D Object Detection Models via Zero-Shot Attention Key PruningCode1
Homography Loss for Monocular 3D Object DetectionCode1
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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