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 1301–1350 of 1576 papers

TitleStatusHype
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
Pixel-Aligned Recurrent Queries for Multi-View 3D Object Detection—0
PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest—0
PointAugmenting: Cross-Modal Augmentation for 3D Object Detection—0
Point Cloud Based Scene Segmentation: A Survey—0
Point-DETR3D: Leveraging Imagery Data with Spatial Point Prior for Weakly Semi-supervised 3D Object Detection—0
PointeNet: A Lightweight Framework for Effective and Efficient Point Cloud Analysis—0
Point Pair Feature based Object Detection for Random Bin Picking—0
PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement—0
PointSee: Image Enhances Point Cloud—0
Polygon Intersection-over-Union Loss for Viewpoint-Agnostic Monocular 3D Vehicle Detection—0
Poster: Making Edge-assisted LiDAR Perceptions Robust to Lossy Point Cloud Compression—0
Pre-trained Trojan Attacks for Visual Recognition—0
PRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for Uncertainty-Aware Object Interaction in Driving Scene Scenario—0
Probabilistic and Geometric Depth: Detecting Objects in Perspective—0
Progressive Multi-Modal Fusion for Robust 3D Object Detection—0
PromptDet: A Lightweight 3D Object Detection Framework with LiDAR Prompts—0
PSA-Det3D: Pillar Set Abstraction for 3D object Detection—0
Pseudo-labeling for Scalable 3D Object Detection—0
PTA-Det: Point Transformer Associating Point cloud and Image for 3D Object Detection—0
PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection—0
PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features—0
PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection—0
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection—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