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 1101–1150 of 1576 papers

TitleStatusHype
MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion—0
MVX-Net: Multimodal VoxelNet for 3D Object Detection—0
Nearest Neighbors Meet Deep Neural Networks for Point Cloud Analysis—0
NeRF-DetS: Enhanced Adaptive Spatial-wise Sampling and View-wise Fusion Strategies for NeRF-based Indoor Multi-view 3D Object Detection—0
Neural Rendering based Urban Scene Reconstruction for Autonomous Driving—0
OA-BEV: Bringing Object Awareness to Bird's-Eye-View Representation for Multi-Camera 3D Object Detection—0
Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection—0
Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots—0
Object-Aware Centroid Voting for Monocular 3D Object Detection—0
Object-Centric Stereo Matching for 3D Object Detection—0
Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding—0
ObjectFusion: Multi-modal 3D Object Detection with Object-Centric Fusion—0
Object Removal Attacks on LiDAR-based 3D Object Detectors—0
Object Viewpoint Classification Based 3D Bounding Box Estimation for Autonomous Vehicles—0
SC3D: Label-Efficient Outdoor 3D Object Detection via Single Click Annotation—0
OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection—0
OCM3D: Object-Centric Monocular 3D Object Detection—0
OcTr: Octree-based Transformer for 3D Object Detection—0
Offboard 3D Object Detection from Point Cloud Sequences—0
Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection—0
One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detection—0
On Offline Evaluation of 3D Object Detection for Autonomous Driving—0
On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery—0
On the Metrics for Evaluating Monocular Depth Estimation—0
On the Robustness of 3D Object Detectors—0
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection—0
Open-set 3D Object Detection—0
HD-OOD3D: Supervised and Unsupervised Out-of-Distribution object detection in LiDAR data—0
Optimisation of the PointPillars network for 3D object detection in point clouds—0
ORA3D: Overlap Region Aware Multi-view 3D Object Detection—0
OriCon3D: Effective 3D Object Detection using Orientation and Confidence—0
Out-of-Distribution Detection for LiDAR-based 3D Object Detection—0
OV-SCAN: Semantically Consistent Alignment for Novel Object Discovery in Open-Vocabulary 3D Object Detection—0
PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection—0
PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds—0
PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving—0
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer—0
Panopticus: Omnidirectional 3D Object Detection on Resource-constrained Edge Devices—0
PatchContrast: Self-Supervised Pre-training for 3D Object Detection—0
Patch Refinement -- Localized 3D Object Detection—0
Pattern-Aware Data Augmentation for LiDAR 3D Object Detection—0
PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection—0
PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points—0
PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector—0
SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving—0
Information Entropy Guided Height-aware Histogram for Quantization-friendly Pillar Feature Encoder—0
PillarHist: A Quantization-aware Pillar Feature Encoder based on Height-aware Histogram—0
PillarMamba: Learning Local-Global Context for Roadside Point Cloud via Hybrid State Space Model—0
PillarNeSt: Embracing Backbone Scaling and Pretraining for Pillar-based 3D Object Detection—0
PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module—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