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 76–100 of 1576 papers

TitleStatusHype
Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event CamerasCode1
LCV2I: Communication-Efficient and High-Performance Collaborative Perception Framework with Low-Resolution LiDAR—0
Q-PETR: Quant-aware Position Embedding Transformation for Multi-View 3D Object Detection—0
Depth-aware Fusion Method based on Image and 4D Radar Spectrum for 3D Object Detection—0
Synth It Like KITTI: Synthetic Data Generation for Object Detection in Driving ScenariosCode0
LXLv2: Enhanced LiDAR Excluded Lean 3D Object Detection with Fusion of 4D Radar and Camera—0
RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection—0
Task-Oriented Semantic Communication for Stereo-Vision 3D Object Detection—0
CoDiff: Conditional Diffusion Model for Collaborative 3D Object DetectionCode1
Text-guided Sparse Voxel Pruning for Efficient 3D Visual GroundingCode3
Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception—0
DetVPCC: RoI-based Point Cloud Sequence Compression for 3D Object Detection—0
SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and DatasetCode1
Improving Generalization Ability for 3D Object Detection by Learning Sparsity-invariant FeaturesCode0
Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection—0
SpikingRTNH: Spiking Neural Network for 4D Radar Object DetectionCode0
IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain—0
Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception—0
Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection—0
LiFT: Lightweight, FPGA-tailored 3D object detection based on LiDAR dataCode0
MutualForce: Mutual-Aware Enhancement for 4D Radar-LiDAR 3D Object Detection—0
MonoSOWA: Scalable monocular 3D Object detector Without human Annotations—0
The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning—0
CoreNet: Conflict Resolution Network for Point-Pixel Misalignment and Sub-Task Suppression of 3D LiDAR-Camera Object DetectionCode1
AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR DataCode1
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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