learning-topology-synthetic-data VS calibrated-backprojection-network

Compare learning-topology-synthetic-data vs calibrated-backprojection-network and see what are their differences.

learning-topology-synthetic-data

Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021) (by alexklwong)
Machine Learning Computer Vision Deep Learning Tensorflow depth depth-estimation depth-completion unsupervised-learning sensor-fusion 3d-vision 3d-reconstruction kitti Void self-supervised-learning Ral icra ucla
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calibrated-backprojection-network

PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021) (by alexklwong)
Machine Learning Deep Learning Computer Vision 3d-vision sensor-fusion unsupervised-learning self-supervised-learning depth-estimation depth-completion 3d-reconstruction Calibration backprojection iccv iccv2021 kitti-dataset void-dataset Pytorch ucla kitti Void
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learning-topology-synthetic-data calibrated-backprojection-network
5 3
37 107
- -
4.3 0.0
9 months ago 9 months ago
Python Python
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
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learning-topology-synthetic-data

Posts with mentions or reviews of learning-topology-synthetic-data. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-24.

calibrated-backprojection-network

Posts with mentions or reviews of calibrated-backprojection-network. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-13.
  • ICCV2021 oral paper improves generalization across sensor platforms
    1 project | news.ycombinator.com | 13 Oct 2021
    Our work "Unsupervised Depth Completion with Calibrated Backprojection Layers" has been accepted as an oral paper at ICCV 2021! We will be giving our talk during Session 10 (10/13 2-3 pm PST / 5-6 pm EST and 10/15 7-8 am PST / 10-11 am EST, https://www.eventscribe.net/2021/ICCV/fsPopup.asp?efp=WlJFS0tHTEMxNTgzMA%20&PosterID=428697%20&rnd=0.4100732&mode=posterinfo). This is joint work with Stefano Soatto at the UCLA Vision Lab.

    In a nutshell: we propose a method for point cloud densification (from camera, IMU, range sensor) that can generalize well across different sensor platforms. The figure in this link illustrates our improvement over existing works: https://github.com/alexklwong/calibrated-backprojection-network/blob/master/figures/overview_teaser.gif

    The slightly longer version: previous methods, when trained on one sensor platform, have problem generalizing to different ones when deployed to the wild. This is because they are overfitted to the sensors used to collect the training set. Our method takes image, sparse point cloud and camera calibration as input, which allows us to use a different calibration at test time. This significantly improves generalization to novel scenes captured by sensors different than those used during training. Amongst our innovations is a "calibrated backprojection layer" that imposes strong inductive bias on the network (as opposed trying to learn everything from the data). This design allows our method to achieve the state of the art on both indoor and outdoor scenarios while using a smaller model size and boasting a faster inference time.

    For those interested, here are the links to

    paper: https://arxiv.org/pdf/2108.10531.pdf

    code (pytorch): https://github.com/alexklwong/calibrated-backprojection-network

  • [R] ICCV2021 oral paper -- Unsupervised Depth Completion with Calibrated Backprojection Layers improves generalization across sensor platforms
    2 projects | /r/MachineLearning | 13 Oct 2021
    Code for https://arxiv.org/abs/2108.10531 found: https://github.com/alexklwong/calibrated-backprojection-network

What are some alternatives?

When comparing learning-topology-synthetic-data and calibrated-backprojection-network you can also consider the following projects:

d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

EasyCV - An all-in-one toolkit for computer vision

bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)

unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)

student-teacher-anomaly-detection - Student–Teacher Anomaly Detection with Discriminative Latent Embeddings

manydepth - [CVPR 2021] Self-supervised depth estimation from short sequences

DECA - DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021)

NeuralRecon - Code for "NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video", CVPR 2021 oral

mmselfsup - OpenMMLab Self-Supervised Learning Toolbox and Benchmark

Make-It-3D - [ICCV 2023] Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior

STEPS - This is the official repository for ICRA-2023 paper "STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation"

learning-topology-synthetic-data vs d2l-en calibrated-backprojection-network vs EasyCV learning-topology-synthetic-data vs bpycv calibrated-backprojection-network vs unsupervised-depth-completion-visual-inertial-odometry learning-topology-synthetic-data vs student-teacher-anomaly-detection calibrated-backprojection-network vs manydepth learning-topology-synthetic-data vs DECA calibrated-backprojection-network vs NeuralRecon learning-topology-synthetic-data vs unsupervised-depth-completion-visual-inertial-odometry calibrated-backprojection-network vs mmselfsup learning-topology-synthetic-data vs Make-It-3D calibrated-backprojection-network vs STEPS