3D Object Detection Models
Deep Stereo Geometry Network
Introduced by Chen et al. in DSGN: Deep Stereo Geometry Network for 3D Object DetectionDeep Stereo Geometry Network is a 3D object detection pipeline that relies on space transformation from 2D features to an effective 3D structure, called 3D geometric volume (3DGV). The whole neural network consists of four components. (a) A 2D image feature extractor for capture of both pixel- and high-level feature. (b) Constructing the plane-sweep volume and 3D geometric volume. (c) Depth Estimation on the plane-sweep volume. (d) 3D object detection on 3D geometric volume.
Source: DSGN: Deep Stereo Geometry Network for 3D Object Detection
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| 3D Object Detection | 3 | 30.00% |
| 3D Object Detection From Stereo Images | 2 | 20.00% |
| Object Detection | 2 | 20.00% |
| Autonomous Driving | 1 | 10.00% |
| Autonomous Vehicles | 1 | 10.00% |
| Vehicle Pose Estimation | 1 | 10.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
