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 1151–1200 of 1576 papers

TitleStatusHype
PointSee: Image Enhances Point Cloud—0
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection—0
Multi-Camera Calibration Free BEV Representation for 3D Object Detection—0
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision—0
MSF3DDETR: Multi-Sensor Fusion 3D Detection Transformer for Autonomous Driving—0
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations—0
IDD-3D: Indian Driving Dataset for 3D Unstructured Road ScenesCode0
LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in TransformersCode0
XC: Exploring Quantitative Use Cases for Explanations in 3D Object DetectionCode0
PSA-Det3D: Pillar Set Abstraction for 3D object Detection—0
Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection—0
Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training—0
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection—0
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining—0
SAILOR: Scaling Anchors via Insights into Latent Object RepresentationCode0
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds—0
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds—0
Bridged Transformer for Vision and Point Cloud 3D Object DetectionCode0
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment—0
Out-of-Distribution Detection for LiDAR-based 3D Object Detection—0
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection—0
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents—0
Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects—0
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars—0
FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object DetectionCode0
Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR—0
Rethinking Dimensionality Reduction in Grid-based 3D Object Detection—0
Self-supervised 3D Object Detection from Monocular Pseudo-LiDAR—0
LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception—0
FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection—0
CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer—0
CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection—0
Multi-modal Streaming 3D Object Detection—0
MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object DetectionCode0
SEFormer: Structure Embedding Transformer for 3D Object Detection—0
Scatter Points in Space: 3D Detection from Multi-view Monocular Images—0
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning—0
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection—0
Anytime-Lidar: Deadline-aware 3D Object Detection—0
Bridging the View Disparity Between Radar and Camera Features for Multi-modal Fusion 3D Object Detection—0
AGO-Net: Association-Guided 3D Point Cloud Object Detection Network—0
Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking—0
Minkowski Tracker: A Sparse Spatio-Temporal R-CNN for Joint Object Detection and Tracking—0
STS: Surround-view Temporal Stereo for Multi-view 3D Detection—0
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object DetectionCode0
Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes—0
GraVoS: Voxel Selection for 3D Point-Cloud DetectionCode0
An Empirical Study of Pseudo-Labeling for Image-based 3D Object Detection—0
Aerial Monocular 3D Object DetectionCode0
Global Hierarchical Attention for 3D Point Cloud Analysis—0
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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