3D Object Detection
585 papers with code • 55 benchmarks • 48 datasets
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 )
Benchmarks
These leaderboards are used to track progress in 3D Object Detection
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Libraries
Use these libraries to find 3D Object Detection models and implementations
KITTI
nuScenes
ScanNet
NYUv2
SUN RGB-D
S3DIS
Waymo Open Dataset
Argoverse
Argoverse 2
ONCE
Subtasks
Latest papers with no code
ContextualFusion: Context-Based Multi-Sensor Fusion for 3D Object Detection in Adverse Operating Conditions
The fusion of multimodal sensor data streams such as camera images and lidar point clouds plays an important role in the operation of autonomous vehicles (AVs).
NeRF-DetS: Enhancing Multi-View 3D Object Detection with Sampling-adaptive Network of Continuous NeRF-based Representation
As a preliminary work, NeRF-Det unifies the tasks of novel view synthesis and 3D perception, demonstrating that perceptual tasks can benefit from novel view synthesis methods like NeRF, significantly improving the performance of indoor multi-view 3D object detection.
Language-Driven Active Learning for Diverse Open-Set 3D Object Detection
In this paper, we propose VisLED, a language-driven active learning framework for diverse open-set 3D Object Detection.
A Point-Based Approach to Efficient LiDAR Multi-Task Perception
Unlike other LiDAR-based multi-task architectures, our proposed PAttFormer does not require separate feature encoders for multiple task-specific point cloud representations, resulting in a network that is 3x smaller and 1. 4x faster while achieving competitive performance on the nuScenes and KITTI benchmarks for autonomous driving perception.
Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection
The integration of Light Detection and Ranging (LiDAR) and Internet of Things (IoT) technologies offers transformative opportunities for public health informatics in urban safety and pedestrian well-being.
TempBEV: Improving Learned BEV Encoders with Combined Image and BEV Space Temporal Aggregation
These results indicate the overall effectiveness of our approach and make a strong case for aggregating temporal information in both image and BEV latent spaces.
Multimodal 3D Object Detection on Unseen Domains
To this end, we propose CLIX$^\text{3D}$, a multimodal fusion and supervised contrastive learning framework for 3D object detection that performs alignment of object features from same-class samples of different domains while pushing the features from different classes apart.
Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection
This can enable improved performance in downstream tasks that are equivariant to such transformations.
VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection
Therefore, an effective solution involves transforming monocular images into LiDAR-like representations and employing a LiDAR-based 3D object detector to predict the 3D coordinates of objects.
Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patterns
To address the real-time operation requirements in ADS, we also introduce a novel introspection method that combines activation patterns from multiple layers of the detector's backbone and report its performance.
