3D Object Detection
583 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.
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Libraries
Use these libraries to find 3D Object Detection models and implementationsSubtasks
Most implemented papers
Multimodal Token Fusion for Vision Transformers
Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images.
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only.
Exploring Data Augmentation for Multi-Modality 3D Object Detection
Due to the fact that multi-modality data augmentation must maintain consistency between point cloud and images, recent methods in this field typically use relatively insufficient data augmentation.
FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection
In this paper, we study this problem with a practice built on a fully convolutional single-stage detector and propose a general framework FCOS3D.
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems.
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications.
AFDet: Anchor Free One Stage 3D Object Detection
High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving.
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely.
CubeSLAM: Monocular 3D Object SLAM
Objects can provide long-range geometric and scale constraints to improve camera pose estimation and reduce monocular drift.
Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
In this paper, we take a slightly different viewpoint -- we find that precise positioning of raw points is not essential for high performance 3D object detection and that the coarse voxel granularity can also offer sufficient detection accuracy.