Comments (2)
from digital_image_processing.
Depth completion aims to recover dense depth maps from sparse depth measurements. It is of increasing importance for autonomous driving and draws increasing attention from the vision community. Most of the current competitive methods directly train a network to learn a mapping from sparse depth inputs to dense depth maps, which has difficulties in utilizing the 3D geometric constraints and handling the practical sensor noises. In this paper, to regularize the depth completion and improve the robustness against noise, we propose a unified CNN framework that 1) models the geometric constraints between depth and surface normal in a diffusion module and 2) predicts the confidence of sparse LiDAR measurements to mitigate the impact of noise. Specifically, our encoder-decoder backbone predicts the surface normal, coarse depth and confidence of LiDAR inputs simultaneously, which are subsequently inputted into our diffusion refinement module to obtain the final completion results. Extensive experiments on KITTI depth completion dataset and NYU-Depth-V2 dataset demonstrate that our method achieves state-of-the-art performance. Further ablation study and analysis give more insights into the proposed components and demonstrate the generalization capability and stability of our model.
from digital_image_processing.
Related Issues (15)
- Grayscale And Normal Guided Depth Completion With A Low-Cost Lidar HOT 2
- Sparsity invariant cnns HOT 2
- Dynamic Spatial Propagation Network for Depth Completion HOT 3
- A comparative review of plausible hole filling strategies in the context of scene depth image completion HOT 2
- A survey on deep learning techniques for stereo-based depth estimation HOT 2
- Depth map artefacts reduction: a review HOT 2
- Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camera HOT 2
- Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty HOT 1
- LIDAR and Monocular Camera Fusion: On-road Depth Completion for Autonomous Driving HOT 2
- Deep Adaptive LiDAR: End-to-end Optimization of Sampling and Depth Completion at Low Sampling Rates HOT 2
- UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion HOT 2
- Radar-Camera Pixel Depth Association for Depth Completion HOT 2
- Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Camera HOT 2
- From Depth What Can You See? Depth Completion via Auxiliary Image Reconstruction HOT 2
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from digital_image_processing.