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 751–800 of 1576 papers

TitleStatusHype
LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal FusionCode0
Virtual Sparse Convolution for Multimodal 3D Object DetectionCode2
X^3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection—0
BSH-Det3D: Improving 3D Object Detection with BEV Shape HeatmapCode1
Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View—0
Nearest Neighbors Meet Deep Neural Networks for Point Cloud Analysis—0
AdaptiveShape: Solving Shape Variability for 3D Object Detection with Geometry Aware Anchor Distributions—0
DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving—0
Pillar R-CNN for Point Cloud 3D Object DetectionCode2
Introducing Depth into Transformer-based 3D Object Detection—0
Transformer-Based Sensor Fusion for Autonomous Driving: A SurveyCode0
A Flexible Multi-view Multi-modal Imaging System for Outdoor Scenes—0
MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts—0
MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion—0
On the Metrics for Evaluating Monocular Depth Estimation—0
Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge—0
3M3D: Multi-view, Multi-path, Multi-representation for 3D Object Detection—0
Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection—0
Surface-biased Multi-Level Context 3D Object Detection—0
Uncertainty-Aware AB3DMOT by Variational 3D Object DetectionCode0
Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving—0
TR3D: Towards Real-Time Indoor 3D Object DetectionCode0
FastPillars: A Deployment-friendly Pillar-based 3D DetectorCode1
Eloss in the way: A Sensitive Input Quality Metrics for Intelligent DrivingCode0
AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation—0
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks—0
LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object DetectionCode1
On the Adversarial Robustness of Camera-based 3D Object DetectionCode1
Exploring Active 3D Object Detection from a Generalization PerspectiveCode1
Unleash the Potential of Image Branch for Cross-modal 3D Object DetectionCode1
PTA-Det: Point Transformer Associating Point cloud and Image for 3D Object Detection—0
SwinDepth: Unsupervised Depth Estimation using Monocular Sequences via Swin Transformer and Densely Cascaded NetworkCode1
DSVT: Dynamic Sparse Voxel Transformer with Rotated SetsCode2
OA-BEV: Bringing Object Awareness to Bird's-Eye-View Representation for Multi-Camera 3D Object Detection—0
Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding—0
FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D DetectionCode1
Rethinking Voxelization and Classification for 3D Object DetectionCode1
Object as Query: Lifting any 2D Object Detector to 3D DetectionCode1
Hierarchical Point Attention for Indoor 3D Object Detection—0
Super Sparse 3D Object Detection—0
StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection—0
MonoEdge: Monocular 3D Object Detection Using Local Perspectives—0
Cross Modal Transformer: Towards Fast and Robust 3D Object DetectionCode3
Argoverse 2: Next Generation Datasets for Self-Driving Perception and ForecastingCode2
Learning from Noisy Data for Semi-Supervised 3D Object DetectionCode1
Query Refinement Transformer for 3D Instance Segmentation—0
3DPPE: 3D Point Positional Encoding for Transformer-based Multi-Camera 3D Object DetectionCode1
MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation—0
A Fast Unified System for 3D Object Detection and TrackingCode1
Towards Universal LiDAR-Based 3D Object Detection by Multi-Domain Knowledge Transfer—0
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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