Abstract—With the rise of data driven deep neural networks as a realization of universal function approximators, most research on computer vision problems has moved away from hand crafted classical image processing algorithms. This paper shows that with a well designed algorithm, we are capable of outperforming neural network based methods on the task of depth completion. The proposed algorithm is simple and fast, runs on the CPU, and relies only on basic image processing operations to perform depth completion of sparse LIDAR depth data. We evaluate our algorithm on the challenging KITTI depth completion benchmark [1], and at the time of submission, our method ranks f irst on the KITTI test server among all published methods. Furthermore, our algorithm is data independent, requiring no training data to perform the task at hand. The code written in Python will be made publicly available at https://github.com/kujason/ip basic.
digital_image_processing's Introduction
digital_image_processing's People
digital_image_processing's Issues
Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty
ICCV 2019
From Depth What Can You See? Depth Completion via Auxiliary Image Reconstruction
LIDAR and Monocular Camera Fusion: On-road Depth Completion for Autonomous Driving
A survey on deep learning techniques for stereo-based depth estimation
Sparsity invariant cnns
UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion
Dynamic Spatial Propagation Network for Depth Completion
Grayscale And Normal Guided Depth Completion With A Low-Cost Lidar
A comparative review of plausible hole filling strategies in the context of scene depth image completion
Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camera
Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Camera
Depth map artefacts reduction: a review
Radar-Camera Pixel Depth Association for Depth Completion
Deep Adaptive LiDAR: End-to-end Optimization of Sampling and Depth Completion at Low Sampling Rates
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