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Implemented "Depth map prediction from a single image using a multi-scale deep network."
A novel approach that estimate Depth-map from Stereo images
Pytorch Implementation of Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
PyTorch implementation of the paper
A convolutional neural network that takes in two stereo images and outputs a depth map with rather reasonable accuracy. The dataset is taken from https://github.com/LouisFoucard/DepthMap_dataset, and the model (15_epoch_model.h5) is only trained through 15 epochs but still produces a believable outcome.
Depth Reconstruction Research: interpolation and super-resolution of depth maps using deep convolutional neural networks.
This is the repo for our new project Highly Accurate Dichotomous Image Segmentation
This paper proposes a deep learning-based method to detect multiple people from a single overhead depth image with high precision. Our neural network, called DPDnet, is composed by two fully-convolutional encoder-decoder blocks built with residual layers. The main block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution, The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and ground truth head position labels. The paper provides a rigorous experimental comparison with some of the best methods of the state-of-the-art, being exhaustively evaluated in different publicly available datasets. DPDnet proves to outperform all the evaluated methods with statistically significant differences, and with accuracies that exceed 99%. The system was trained on one of the datasets (generated by the authors and available to the scientific community) and evaluated in the others without retraining, proving also to achieve high accuracy with varying datasets and experimental conditions. Additionally, we made a comparison of our proposal with other CNN-based alternatives that have been very recently proposed in the literature, obtaining again very high performance. Finally, the computational complexity of our proposal is shown to be independent of the number of users in the scene and runs in real time using conventional GPUs.
Rembg is a tool to remove images background
This is an implementation of Depth Map Prediction from a Single Image using a Multi-Scale Deep Network in TensorFlow 2
Stereo convolutional neural network for depth map prediction from stereo images
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Data-Driven Documents codes.
China tencent open source team.