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 1301–1325 of 1576 papers

TitleStatusHype
TANet++: Triple Attention Network with Filtered Pointcloud on 3D Detection—0
Task-Oriented Semantic Communication for Stereo-Vision 3D Object Detection—0
Teleoperated Driving: a New Challenge for 3D Object Detection in Compressed Point Clouds—0
TempBEV: Improving Learned BEV Encoders with Combined Image and BEV Space Temporal Aggregation—0
Temporal-Channel Transformer for 3D Lidar-Based Video Object Detection in Autonomous Driving—0
The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning—0
The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection—0
The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes—0
The Meeseeks Mesh: Spatially Consistent 3D Adversarial Objects for BEV Detector—0
ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions—0
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration—0
Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients—0
TICaM: A Time-of-flight In-car Cabin Monitoring Dataset—0
Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving—0
Time-to-Label: Temporal Consistency for Self-Supervised Monocular 3D Object Detection—0
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels—0
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration—0
Towards 3D Object Detection with 2D Supervision—0
Towards 3D Object Detection With Bimodal Deep Boltzmann Machines Over RGBD Imagery—0
Towards Autonomous Driving: a Multi-Modal 360^ Perception Proposal—0
Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles—0
Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View—0
Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach—0
Towards Model Generalization for Monocular 3D Object Detection—0
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents—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