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 1326–1350 of 1576 papers

TitleStatusHype
Reinforced Axial Refinement Network for Monocular 3D Object Detection—0
PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges—0
3D for Free: Crossmodal Transfer Learning using HD Maps—0
Towards Autonomous Driving: a Multi-Modal 360^ Perception Proposal—0
Deformable PV-RCNN: Improving 3D Object Detection with Learned DeformationsCode1
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionCode1
Cross-Modality 3D Object Detection—0
Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery—0
SSN: Shape Signature Networks for Multi-class Object Detection from Point CloudsCode1
Distance-Normalized Unified Representation for Monocular 3D Object Detection—0
Monocular 3D Object Detection via Feature Domain Adaptation—0
Rotation-robust Intersection over Union for 3D Object Detection—0
Searching Efficient 3D Architectures with Sparse Point-Voxel ConvolutionCode2
Weakly Supervised 3D Object Detection from Point CloudsCode1
Part-Aware Data Augmentation for 3D Object Detection in Point CloudCode1
An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds—0
Weakly Supervised 3D Object Detection from Lidar Point CloudCode1
Object-Aware Centroid Voting for Monocular 3D Object Detection—0
Pillar-based Object Detection for Autonomous DrivingCode1
Kinematic 3D Object Detection in Monocular VideoCode1
EPNet: Enhancing Point Features with Image Semantics for 3D Object DetectionCode1
InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling—0
CenterNet3D: An Anchor Free Object Detector for Point CloudCode1
Unsupervised object-centric video generation and decomposition in 3DCode0
Wasserstein Distances for Stereo Disparity EstimationCode1
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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