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 1251–1275 of 1576 papers

TitleStatusHype
Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time—0
Single-Shot Monocular RGB-D Imaging Using Uneven Double Refraction—0
Towards Generalization Across Depth for Monocular 3D Object Detection—0
SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar—0
SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labelling—0
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving—0
Sparse Dense Fusion for 3D Object Detection—0
SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based 3D Object Detection—0
SparseDet: Towards End-to-End 3D Object Detection—0
SparseFusion: Efficient Sparse Multi-Modal Fusion Framework for Long-Range 3D Perception—0
Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection—0
SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detection—0
Sparse Points to Dense Clouds: Enhancing 3D Detection with Limited LiDAR Data—0
Sparse-to-Dense LiDAR Point Generation by LiDAR-Camera Fusion for 3D Object Detection—0
SparseVoxFormer: Sparse Voxel-based Transformer for Multi-modal 3D Object Detection—0
SpatialLM: Training Large Language Models for Structured Indoor Modeling—0
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection—0
SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation—0
SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding—0
SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving—0
SpikingRTNH: Spiking Neural Network for 4D Radar Object Detection—0
SpotNet: An Image Centric, Lidar Anchored Approach To Long Range Perception—0
SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations—0
SS3D: Single Shot 3D Object Detector—0
SS3D: Sparsely-Supervised 3D Object Detection From Point Cloud—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