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 1451–1500 of 1576 papers

TitleStatusHype
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
SSC3OD: Sparsely Supervised Collaborative 3D Object Detection from LiDAR Point Clouds—0
SSF3D: Strict Semi-Supervised 3D Object Detection with Switching Filter—0
ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection—0
STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning—0
StarNet: Targeted Computation for Object Detection in Point Clouds—0
Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection—0
STD: Sparse-to-Dense 3D Object Detector for Point Cloud—0
Stereo CenterNet based 3D Object Detection for Autonomous Driving—0
StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection—0
Stereo Frustums: A Siamese Pipeline for 3D Object Detection—0
Stereo Object Matching Network—0
Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection—0
Structure Aware and Class Balanced 3D Object Detection on nuScenes Dataset—0
Structure Information is the Key: Self-Attention RoI Feature Extractor in 3D Object Detection—0
STS: Surround-view Temporal Stereo for Multi-view 3D Detection—0
Study of Dropout in PointPillars with 3D Object Detection—0
SUIT: Learning Significance-guided Information for 3D Temporal Detection—0
Super Sparse 3D Object Detection—0
Surface-biased Multi-Level Context 3D Object Detection—0
Survey and Systematization of 3D Object Detection Models and Methods—0
SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection—0
SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds—0
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds—0
Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulk—0
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection—0
TANet++: Triple Attention Network with Filtered Pointcloud on 3D Detection—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