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 721–730 of 1576 papers

TitleStatusHype
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving—0
Unified Domain Generalization and Adaptation for Multi-View 3D Object Detection—0
DECADE: Towards Designing Efficient-yet-Accurate Distance Estimation Modules for Collision Avoidance in Mobile Advanced Driver Assistance Systems—0
SAM-Guided Masked Token Prediction for 3D Scene Understanding—0
MambaBEV: An efficient 3D detection model with Mamba2—0
Cocoon: Robust Multi-Modal Perception with Uncertainty-Aware Sensor Fusion—0
CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box PredictionCode0
ROA-BEV: 2D Region-Oriented Attention for BEV-based 3D Object—0
UAV3D: A Large-scale 3D Perception Benchmark for Unmanned Aerial Vehicles—0
HeightFormer: A Semantic Alignment Monocular 3D Object Detection Method from Roadside Perspective—0
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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