FIELD: navigation of mobile robots for mobile applications.
SUBSTANCE: method performs scene understanding and object recognition. The method is proposed in which a point cloud is taken, reconstructed or obtained from depth sensors. First, this point cloud is represented as a voxel representation. The voxel representation is then processed with a 3D sparse convolutional "skeleton". Next, a 3D sparse convolution "neck" is used to extract features at different levels of the "skeleton". The resulting features are processed through a 3D sparse convolutional "head" with weights common to different feature levels. At each feature level, the "head" is responsible for predicting objects of a certain scale. The "head" gives the bounding box options for each position. Finally, all predictions received are filtered to select the most likely ones.
EFFECT: increasing the accuracy of object detection and reducing the scene processing time.
3 cl, 4 dwg, 4 tbl
| Title | Year | Author | Number |
|---|---|---|---|
| METHOD AND ELECTRONIC DEVICE FOR DETECTING THREE-DIMENSIONAL OBJECTS USING NEURAL NETWORKS | 2021 |
|
RU2776814C1 |
| NEURAL DOT GRAPHIC | 2019 |
|
RU2729166C1 |
| MODELLING PEOPLE'S CLOTHING BASED ON MULTIPLE POINTS | 2021 |
|
RU2776825C1 |
| METHOD OF SYNTHESIS OF A TWO-DIMENSIONAL IMAGE OF A SCENE VIEWED FROM A REQUIRED VIEW POINT AND ELECTRONIC COMPUTING APPARATUS FOR IMPLEMENTATION THEREOF | 2020 |
|
RU2749749C1 |
| METHOD FOR INTERACTIVE SEGMENTATION OF OBJECT ON IMAGE AND ELECTRONIC COMPUTING DEVICE FOR REALIZING SAID OBJECT | 2020 |
|
RU2742701C1 |
| METHOD FOR 3D RECONSTRUCTION OF A HUMAN HEAD TO OBTAIN A RENDER IMAGE OF A PERSON | 2022 |
|
RU2786362C1 |
| TEXTURED NEURAL AVATARS | 2019 |
|
RU2713695C1 |
| METHOD AND SYSTEM FOR REFINING THE CAMERA POSITION TAKING INTO ACCOUNT THE ROOM PLAN | 2022 |
|
RU2794441C1 |
| TRAINING GAN (GENERATIVE ADVERSARIAL NETWORKS) TO CREATE PIXEL-BY-PIXEL ANNOTATION | 2019 |
|
RU2735148C1 |
| METHOD AND SYSTEM FOR REMOTE CLOTHING SELECTION | 2020 |
|
RU2805003C2 |
