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 876–900 of 1576 papers

TitleStatusHype
Eliminating Cross-modal Conflicts in BEV Space for LiDAR-Camera 3D Object DetectionCode0
Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving—0
LVIC: Multi-modality segmentation by Lifting Visual Info as Cue—0
ActFormer: Scalable Collaborative Perception via Active Queries—0
CMDA: Cross-Modal and Domain Adversarial Adaptation for LiDAR-Based 3D Object Detection—0
Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator—0
Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?Code0
FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird's-Eye View and Perspective View—0
False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy—0
Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample SelectionCode0
Towards Unified 3D Object Detection via Algorithm and Data Unification—0
A Vanilla Multi-Task Framework for Dense Visual Prediction Solution to 1st VCL Challenge -- Multi-Task Robustness Track—0
AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene UnderstandingCode0
SDGE: Stereo Guided Depth Estimation for 360^ Camera Sets—0
Neural Rendering based Urban Scene Reconstruction for Autonomous Driving—0
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration—0
Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private SourcesCode0
Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective—0
A Survey for Foundation Models in Autonomous Driving—0
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration—0
You Only Look Bottom-Up for Monocular 3D Object Detection—0
Pragmatic Communication in Multi-Agent Collaborative PerceptionCode0
MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection—0
Large receptive field strategy and important feature extraction strategy in 3D object detection—0
CascadeV-Det: Cascade Point Voting for 3D Object DetectionCode0
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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