From ad0f0e0814458990a0e5736cba91da9a849471c8 Mon Sep 17 00:00:00 2001 From: Leonard Bruns Date: Mon, 11 Jul 2022 07:49:02 +0200 Subject: [PATCH] Improve README --- README.md | 21 +++++++++--------- .../preprocess_shapenet.sh | 0 .../reproduce_paper.sh | 0 .../train_init_networks.sh | 0 .../train_vaes.sh | 0 resources/sdfest_overview.gif | Bin 0 -> 2412170 bytes 6 files changed, 11 insertions(+), 10 deletions(-) rename {reproduce_scripts => reproduction_scripts}/preprocess_shapenet.sh (100%) rename {reproduce_scripts => reproduction_scripts}/reproduce_paper.sh (100%) rename {reproduce_scripts => reproduction_scripts}/train_init_networks.sh (100%) rename {reproduce_scripts => reproduction_scripts}/train_vaes.sh (100%) create mode 100644 resources/sdfest_overview.gif diff --git a/README.md b/README.md index 5e1dab3..6eb39bd 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,7 @@ SDFEst is a package for pose, scale, and shape estimation using discretized sign [Installation](#installation) | [Dataset Preparation](#dataset-preparation) | [Paper Reproduction](#paper-reproduction) | [Code Structure](#code-structure) | [Development](#development) | [Citation](#citation) | [Docs](https://roym899.github.io/sdfest/) +![Animation of differentiable renderer.](resources/sdfest_overview.gif) ## Installation @@ -34,7 +35,7 @@ pip install -r requirements.txt -e . You need to install Detectron2 manually to run the pipeline with automatic instance segmentation. Follow the [detectron2 installation guide](https://detectron2.readthedocs.io/en/latest/tutorials/install.html) from there. -Tested with detectron2 0.5 + torch 1.9.0. +Tested with detectron2 0.6 + torch 1.12.0 (and various older versions). ## Dataset Preparation See below for expected folder structure for each dataset. @@ -71,14 +72,14 @@ See below for expected folder structure for each dataset. First, make sure the datasets are in the right format. -### Benchmark Results -Depending on which dataset, you have downloaded you can reproduce the results reported in the paper (using the already trained models) by running the script -```bash -source reproduce_{shapenet,modelnet,redwood}_experiments.sh -``` -after that, all results can be found in `./results`. +### Full paper (training and experiments) +See the bash script in `reproduction_scripts/reproduce_paper.sh`. + +Evaluation code for REAL275 and REDWOOD75 experiments will be integrated in [cpas_toolbox](https://github.com/roym899/pose_and_shape_evaluation) soon. + +Non-cleaned up version of evaluation code can be found in `icaps_eval` branch. -### Train Models +### Train Models Only To train a network for a specific category you need to first train a per-category VAE, and *afterwards* an initialization network. #### VAE First we need to convert the ShapeNet meshes to SDFs and optionally filter the dataset. To reproduce the preprocessing of the paper run @@ -91,7 +92,7 @@ source train_vaes.sh ``` to train the models using the same configuration as used for the paper. -#### Init Network +#### Initialization Network To train the initialization network we used in our paper, run ```bash source train_init_networks.sh @@ -113,7 +114,7 @@ Code is structured into 4 sub-packages: Differentiable rendering of depth image for signed distance fields. -The signed distance field is assumed to be voxelized and it's pose is given by a x, y, z in the camera frame, a quaternion describing its orientation and a scale parameter describing its size. This module provides the derivative with respect to the signed distance values, and the full pose description (position, orientation, scale). +The signed distance field is assumed to be voxelized and its pose is given by a x, y, z in the camera frame, a quaternion describing its orientation and a scale parameter describing its size. This module provides the derivative with respect to the signed distance values, and the full pose description (position, orientation, scale). #### Generating compile_commands.json General workflow for PyTorch extensions (only tested for JIT, probably similar otherwise) diff --git a/reproduce_scripts/preprocess_shapenet.sh b/reproduction_scripts/preprocess_shapenet.sh similarity index 100% rename from reproduce_scripts/preprocess_shapenet.sh rename to reproduction_scripts/preprocess_shapenet.sh diff --git a/reproduce_scripts/reproduce_paper.sh b/reproduction_scripts/reproduce_paper.sh similarity index 100% rename from reproduce_scripts/reproduce_paper.sh rename to reproduction_scripts/reproduce_paper.sh diff --git a/reproduce_scripts/train_init_networks.sh b/reproduction_scripts/train_init_networks.sh similarity index 100% rename from reproduce_scripts/train_init_networks.sh rename to reproduction_scripts/train_init_networks.sh diff --git a/reproduce_scripts/train_vaes.sh 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