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 1151–1200 of 1576 papers

TitleStatusHype
ODAM: Object Detection, Association, and Mapping using Posed RGB VideoCode1
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object DetectionCode1
ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection—0
Semi-supervised 3D Object Detection via Adaptive Pseudo-Labeling—0
SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation—0
PVT: Point-Voxel Transformer for Point Cloud LearningCode1
Is Pseudo-Lidar needed for Monocular 3D Object detection?Code1
Progressive Coordinate Transforms for Monocular 3D Object DetectionCode1
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse WeatherCode1
From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point DecoderCode1
Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point CloudCode1
Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous VehiclesCode1
MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review—0
Angle Based Feature Learning in GNN for 3D Object Detection using Point Cloud—0
From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object DetectionCode1
Probabilistic and Geometric Depth: Detecting Objects in Perspective—0
Geometry Uncertainty Projection Network for Monocular 3D Object DetectionCode1
Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object DetectionCode1
Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth—0
DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization—0
AA3DNet: Attention Augmented Real Time 3D Object Detection—0
Multi-Echo LiDAR for 3D Object Detection—0
Unsupervised Domain Adaptive 3D Detection with Multi-Level ConsistencyCode1
Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras—0
Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object DetectionCode1
Multi-Modality Task Cascade for 3D Object DetectionCode1
VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars—0
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance VotingCode0
MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View UnderstandingCode1
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach—0
Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search—0
Representation Based Regression for Object Distance EstimationCode1
TANet++: Triple Attention Network with Filtered Pointcloud on 3D Detection—0
Real-time 3D Object Detection using Feature Map FlowCode1
Radar Voxel Fusion for 3D Object DetectionCode1
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection—0
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels—0
Multi-Modal 3D Object Detection in Autonomous Driving: a Survey—0
FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object DetectionCode1
3D Object Detection for Autonomous Driving: A SurveyCode1
One Million Scenes for Autonomous Driving: ONCE DatasetCode1
SRDAN: Scale-Aware and Range-Aware Domain Adaptation Network for Cross-Dataset 3D Object DetectionCode1
RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union—0
PointAugmenting: Cross-Modal Augmentation for 3D Object Detection—0
LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection—0
RankDetNet: Delving Into Ranking Constraints for Object Detection—0
PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features—0
Shape Prior Non-Uniform Sampling Guided Real-time Stereo 3D Object Detection—0
DeepCompress: Efficient Point Cloud Geometry CompressionCode0
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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