3D Object Detection
277 papers with code • 38 benchmarks • 26 datasets
2D object detection classifies the object category and estimates oriented 2D bounding boxes of physical objects from 3D sensor data.
( Image credit: AVOD )
Libraries
Use these libraries to find 3D Object Detection models and implementationsMost implemented papers
Frustum PointNets for 3D Object Detection from RGB-D Data
In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
nuScenes: A multimodal dataset for autonomous driving
Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.
Deep Hough Voting for 3D Object Detection in Point Clouds
Current 3D object detection methods are heavily influenced by 2D detectors.
3D Bounding Box Estimation Using Deep Learning and Geometry
In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D bounding box.
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.
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
In this paper, we propose PointRCNN for 3D object detection from raw point cloud.
Center-based 3D Object Detection and Tracking
Three-dimensional objects are commonly represented as 3D boxes in a point-cloud.
PointPillars: Fast Encoders for Object Detection from Point Clouds
These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.
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.