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 426–450 of 1576 papers

TitleStatusHype
3D Small Object Detection with Dynamic Spatial PruningCode1
DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against CorruptionsCode1
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object DetectionCode1
DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward GuidanceCode1
DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic FusionCode1
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation ModelsCode1
MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View UnderstandingCode1
3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance SegmentationCode1
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous DrivingCode1
BSH-Det3D: Improving 3D Object Detection with BEV Shape HeatmapCode1
MetaBEV: Solving Sensor Failures for BEV Detection and Map SegmentationCode1
An End-to-End Transformer Model for 3D Object DetectionCode1
Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode1
3D Object Detection for Autonomous Driving: A SurveyCode1
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR DataCode1
FCOS3D: Fully Convolutional One-Stage Monocular 3D Object DetectionCode1
DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera ScenariosCode1
CaKDP: Category-aware Knowledge Distillation and Pruning Framework for Lightweight 3D Object DetectionCode1
Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object DetectionCode1
FastPillars: A Deployment-friendly Pillar-based 3D DetectorCode1
EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye ViewCode1
FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object DetectionCode1
Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using FusionCode1
Canadian Adverse Driving Conditions DatasetCode1
Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object DetectionCode1
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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