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 1351–1375 of 1576 papers

TitleStatusHype
PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges—0
PV-SSD: A Multi-Modal Point Cloud Feature Fusion Method for Projection Features and Variable Receptive Field Voxel Features—0
PVTransformer: Point-to-Voxel Transformer for Scalable 3D Object Detection—0
Physical Informed Driving World Model—0
QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection—0
Q-PETR: Quant-aware Position Embedding Transformation for Multi-View 3D Object Detection—0
QuadBEV: An Efficient Quadruple-Task Perception Framework via Bird's-Eye-View Representation—0
Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking—0
Quantifying Data Augmentation for LiDAR based 3D Object Detection—0
Query Refinement Transformer for 3D Instance Segmentation—0
RaCFormer: Towards High-Quality 3D Object Detection via Query-based Radar-Camera Fusion—0
RadarPillars: Efficient Object Detection from 4D Radar Point Clouds—0
R-AGNO-RPN: A LIDAR-Camera Region Deep Network for Resolution-Agnostic Detection—0
Range Adaptation for 3D Object Detection in LiDAR—0
RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union—0
RankDetNet: Delving Into Ranking Constraints for Object Detection—0
RayFormer: Improving Query-Based Multi-Camera 3D Object Detection via Ray-Centric Strategies—0
RCBEVDet++: Toward High-accuracy Radar-Camera Fusion 3D Perception Network—0
RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion—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
DeepMix: Mobility-aware, Lightweight, and Hybrid 3D Object Detection for Headsets—0
Real-time 3D object proposal generation and classification under limited processing resources—0
Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation—0
Real-Time And Robust 3D Object Detection with Roadside LiDARs—0
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All datasetsnuScenesScanNetV2SUN-RGBD valKITTI Cars EasyKITTI Cars HardnuScenes Camera OnlyKITTI Cars Easy valKITTI Cars Moderate valnuscenes Camera-RadarView-of-Delft (val)KITTI Cars Hard valDAIR-V2X-I

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