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 701–725 of 1576 papers

TitleStatusHype
Open-Vocabulary Point-Cloud Object Detection without 3D AnnotationCode1
Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object PredictionCode1
VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object DetectionCode1
EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object DetectionCode2
IC-FPS: Instance-Centroid Faster Point Sampling Module for 3D Point-base Object Detection—0
SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision TransformerCode1
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions—0
BEVFusion4D: Learning LiDAR-Camera Fusion Under Bird's-Eye-View via Cross-Modality Guidance and Temporal Aggregation—0
Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous DrivingCode1
DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and TrackingCode1
SimDistill: Simulated Multi-modal Distillation for BEV 3D Object DetectionCode1
LinK: Linear Kernel for LiDAR-based 3D PerceptionCode1
Learning to Zoom and Unzoom—0
Unsupervised Adaptation from Repeated Traversals for Autonomous DrivingCode0
UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye ViewCode1
Viewpoint Equivariance for Multi-View 3D Object DetectionCode1
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown ObjectsCode3
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation—0
MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer—0
MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-TrainingCode1
OcTr: Octree-based Transformer for 3D Object Detection—0
EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose EstimationCode3
Spherical Transformer for LiDAR-based 3D RecognitionCode2
Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object DetectionCode2
DR.CPO: Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR OcclusionCode0
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All datasetsnuScenesScanNetV2SUN-RGBD valKITTI Cars EasyKITTI Cars HardnuScenes Camera OnlyKITTI Cars Easy valKITTI Cars Moderate valnuscenes Camera-RadarView-of-Delft (val)KITTI Cars Hard valDAIR-V2X-I

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