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 1176–1200 of 1576 papers

TitleStatusHype
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
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