Official implementation of NeuralFusion: Online Depth Map Fusion in Latent Space

Overview

NeuralFusion

This is the official implementation of NeuralFusion: Online Depth Map Fusion in Latent Space. We provide code to train the proposed pipeline on ShapeNet, ModelNet, as well as Tanks and Temples.

If you plan to use NeuralFusion for commercial purposes, please contact the author first. For more information, please also see the license.

Installation

Install the code using the following steps

conda env create -f environment.yml
conda activate neural-fusion

Data Preparation

In order to prepare the data, please follow the instructions explained in this repo.

Training

In order to train the pipeline, run the following

python train.py --experiment_path /path/where/you/want/to/save/the/experiment \ 
                --data_path /path/to/your/data \
                --config configs/train/your/config.yaml

Testing

In order to test the pipeline, run the following

python test.py --test /path/to/your/test/config \
               --root_path /path/where/you/saved/your/experiments \
               --data_path /path/to/your/data \
               --experiment $experiment_name \ 
               --version $experiment_version \
               --checkpoint $experiment_checkpoint

For example, if you would like to test the pretrained on ShapeNet, you need to run the following command

export DATA_PATH=/path/to/your/preprocessed/shapenet/data


python test.py --test configs/test/shapenet/shapenet.noise.005.yaml \
               --root_path pretrained_models \
               --data_path $DATA_PATH \
               --experiment shapenet_noise_005 \ 
               --version 0 \
               --checkpoint best.ckpt
You might also like...
Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals
Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals

LapDepth-release This repository is a Pytorch implementation of the paper "Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals" M

 Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021)
Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021)

Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021) Kranti Kumar Parida, Siddharth Srivastava, Gaurav Sharma. We address the pr

Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in ONNX
Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in ONNX

ONNX msg_chn_wacv20 depth completion Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20 model in

Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in Tensorflow Lite.
Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in Tensorflow Lite.

TFLite-msg_chn_wacv20-depth-completion Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model

Light-weight network, depth estimation, knowledge distillation, real-time depth estimation, auxiliary data.
Light-weight network, depth estimation, knowledge distillation, real-time depth estimation, auxiliary data.

light-weight-depth-estimation Boosting Light-Weight Depth Estimation Via Knowledge Distillation, https://arxiv.org/abs/2105.06143 Junjie Hu, Chenyou F

Data-depth-inference - Data depth inference with python
Data-depth-inference - Data depth inference with python

Welcome! This readme will guide you through the use of the code in this reposito

(CVPR 2022 - oral) Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry
(CVPR 2022 - oral) Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry

Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry Official implementation of the paper Multi-View Depth Est

Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

CLIP-GLaSS Repository for the paper Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search An in-browser demo is

Face Identity Disentanglement via Latent Space Mapping [SIGGRAPH ASIA 2020]
Face Identity Disentanglement via Latent Space Mapping [SIGGRAPH ASIA 2020]

Face Identity Disentanglement via Latent Space Mapping Description Official Implementation of the paper Face Identity Disentanglement via Latent Space

Comments
  • Graphics, voxelgrid defination is missing

    Graphics, voxelgrid defination is missing

    Please add steps about deps required for the codebase I got this error for while test.py Error: File "~/Working/Projects/NeuralFusion/training/database.py", line 7, in from graphics.voxelgrid import FeatureGrid, Voxelgrid ModuleNotFoundError: No module named 'graphics'

    I add the deps and scripts folder to the repo and build the deps as in routedfusion using install_docker.sh, then ran the test.py got this error.

    File "~/Working/Projects/NeuralFusion/deps/graphics/src/graphics/voxelgrid.py", line 28, in init self._data = np.zeros(self._shape) TypeError: only integer scalar arrays can be converted to a scalar index

    Is there a difference in voxelgrid defination ?

    opened by chinmay1148 2
Owner
PhD student at ETH Zurich mainly focusing on computer vision, machine learning and artificial intelligence.
null
Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Face Identity Disentanglement via Latent Space Mapping - Implement in pytorch with StyleGAN 2 Description Pytorch implementation of the paper Face Ide

Daniel Roich 58 Dec 24, 2022
Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments

Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments Paper: arXiv (ICRA 2021) Video : https://youtu.be/CC

Sachini Herath 68 Jan 3, 2023
PyTorch implementation of the WarpedGANSpace: Finding non-linear RBF paths in GAN latent space (ICCV 2021)

Authors official PyTorch implementation of the "WarpedGANSpace: Finding non-linear RBF paths in GAN latent space" [ICCV 2021].

Christos Tzelepis 100 Dec 6, 2022
Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

HamasKhan 3 Jul 8, 2022
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral)

DSA^2 F: Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral) This repo is the official imp

如今我已剑指天涯 46 Dec 21, 2022
Office source code of paper UniFuse: Unidirectional Fusion for 360$^\circ$ Panorama Depth Estimation

UniFuse (RAL+ICRA2021) Office source code of paper UniFuse: Unidirectional Fusion for 360$^\circ$ Panorama Depth Estimation, arXiv, Demo Preparation I

Alibaba 47 Dec 26, 2022
This repo is for Self-Supervised Monocular Depth Estimation with Internal Feature Fusion(arXiv), BMVC2021

DIFFNet This repo is for Self-Supervised Monocular Depth Estimation with Internal Feature Fusion(arXiv), BMVC2021 A new backbone for self-supervised d

Hang 3 Oct 22, 2021
Honours project, on creating a depth estimation map from two stereo images of featureless regions

image-processing This module generates depth maps for shape-blocked-out images Install If working with anaconda, then from the root directory: conda e

null 2 Oct 17, 2022
Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models

merged_depth runs (1) AdaBins, (2) DiverseDepth, (3) MiDaS, (4) SGDepth, and (5) Monodepth2, and calculates a weighted-average per-pixel absolute dept

Pranav 39 Nov 21, 2022
The implemention of Video Depth Estimation by Fusing Flow-to-Depth Proposals

Flow-to-depth (FDNet) video-depth-estimation This is the implementation of paper Video Depth Estimation by Fusing Flow-to-Depth Proposals Jiaxin Xie,

null 32 Jun 14, 2022