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 926–950 of 1576 papers

TitleStatusHype
Learned Fusion: 3D Object Detection using Calibration-Free Transformer Feature Fusion—0
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning—0
SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining Environments: A Novel Dataset and ROS-Based Automatic Annotation Pipeline—0
Mitigating Perspective Distortion-induced Shape Ambiguity in Image Crops—0
ScAR: Scaling Adversarial Robustness for LiDAR Object DetectionCode0
Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving—0
Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach—0
PillarNeSt: Embracing Backbone Scaling and Pretraining for Pillar-based 3D Object Detection—0
Towards Transferable Multi-modal Perception Representation Learning for Autonomy: NeRF-Supervised Masked AutoEncoder—0
Domain Generalization of 3D Object Detection by Density-ResamplingCode0
Refining the ONCE Benchmark with Hyperparameter Tuning—0
Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey—0
3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features—0
FusionViT: Hierarchical 3D Object Detection via LiDAR-Camera Vision Transformer Fusion—0
mmFUSION: Multimodal Fusion for 3D Objects Detection—0
3DifFusionDet: Diffusion Model for 3D Object Detection with Robust LiDAR-Camera Fusion—0
M&M3D: Multi-Dataset Training and Efficient Network for Multi-view 3D Object DetectionCode0
CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar—0
Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision—0
ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object DetectionCode0
MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection—0
DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection—0
Pre-Training LiDAR-Based 3D Object Detectors Through ColorizationCode0
Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS—0
RTNH+: Enhanced 4D Radar Object Detection Network using Combined CFAR-based Two-level Preprocessing and Vertical Encoding—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