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 1351–1400 of 1576 papers

TitleStatusHype
Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection—0
UniBEVFusion: Unified Radar-Vision BEVFusion for 3D Object Detection—0
Unified Domain Generalization and Adaptation for Multi-View 3D Object Detection—0
UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection—0
Towards Unified 3D Object Detection via Algorithm and Data Unification—0
Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation—0
Unlocking Textual and Visual Wisdom: Open-Vocabulary 3D Object Detection Enhanced by Comprehensive Guidance from Text and Image—0
Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training—0
UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles—0
UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenes—0
UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detection—0
V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions—0
ParCon: Noise-Robust Collaborative Perception via Multi-module Parallel Connection—0
V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection—0
VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation—0
VENet: Voting Enhancement Network for 3D Object Detection—0
VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection—0
View Invariant Human Body Detection and Pose Estimation from Multiple Depth Sensors—0
View-to-Label: Multi-View Consistency for Self-Supervised 3D Object Detection—0
ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network—0
VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection—0
VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars—0
VirtualPainting: Addressing Sparsity with Virtual Points and Distance-Aware Data Augmentation for 3D Object Detection—0
Vision-based Lifting of 2D Object Detections for Automated Driving—0
VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving—0
Visual-Inertial-Semantic Scene Representation for 3D Object Detection—0
VLMine: Long-Tail Data Mining with Vision Language Models—0
V-MIND: Building Versatile Monocular Indoor 3D Detector with Diverse 2D Annotations—0
VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view—0
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks—0
Voting for Voting in Online Point Cloud Object Detection—0
Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds—0
VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection—0
VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion—0
VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection—0
Voxel or Pillar: Exploring Efficient Point Cloud Representation for 3D Object Detection—0
Weak Cube R-CNN: Weakly Supervised 3D Detection using only 2D Bounding Boxes—0
Weakly Supervised Point Clouds Transformer for 3D Object Detection—0
Weighted Unsupervised Learning for 3D Object Detection—0
Which2comm: An Efficient Collaborative Perception Framework for 3D Object Detection—0
X^3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection—0
X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection—0
X-view: Non-egocentric Multi-View 3D Object Detector—0
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud—0
You Don't Only Look Once: Constructing Spatial-Temporal Memory for Integrated 3D Object Detection and Tracking—0
You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning—0
You Only Look Bottom-Up for Monocular 3D Object Detection—0
ZFusion: An Effective Fuser of Camera and 4D Radar for 3D Object Perception in Autonomous Driving—0
ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving—0
Scan2Cap: Context-aware Dense Captioning in RGB-D Scans—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MegFusionNDS0.77—Unverified
3MMFusion-eNDS0.77—Unverified
4BEVFusion-eNDS0.76—Unverified
5RacoonPowerNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8FusionVPENDS0.75—Unverified
9FocalFormer3D-FNDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified