The Surprising Effectiveness of Diffusion Models for Optical Flow and
Monocular Depth Estimation
- URL: http://arxiv.org/abs/2306.01923v2
- Date: Wed, 6 Dec 2023 04:19:29 GMT
- Title: The Surprising Effectiveness of Diffusion Models for Optical Flow and
Monocular Depth Estimation
- Authors: Saurabh Saxena, Charles Herrmann, Junhwa Hur, Abhishek Kar, Mohammad
Norouzi, Deqing Sun, David J. Fleet
- Abstract summary: Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity.
We show that they also excel in estimating optical flow and monocular depth, surprisingly, without task-specific architectures and loss functions.
- Score: 42.48819460873482
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Denoising diffusion probabilistic models have transformed image generation
with their impressive fidelity and diversity. We show that they also excel in
estimating optical flow and monocular depth, surprisingly, without
task-specific architectures and loss functions that are predominant for these
tasks. Compared to the point estimates of conventional regression-based
methods, diffusion models also enable Monte Carlo inference, e.g., capturing
uncertainty and ambiguity in flow and depth. With self-supervised pre-training,
the combined use of synthetic and real data for supervised training, and
technical innovations (infilling and step-unrolled denoising diffusion
training) to handle noisy-incomplete training data, and a simple form of
coarse-to-fine refinement, one can train state-of-the-art diffusion models for
depth and optical flow estimation. Extensive experiments focus on quantitative
performance against benchmarks, ablations, and the model's ability to capture
uncertainty and multimodality, and impute missing values. Our model, DDVM
(Denoising Diffusion Vision Model), obtains a state-of-the-art relative depth
error of 0.074 on the indoor NYU benchmark and an Fl-all outlier rate of 3.26\%
on the KITTI optical flow benchmark, about 25\% better than the best published
method. For an overview see https://diffusion-vision.github.io.
Related papers
- Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method [9.173055778539641]
We propose a principled expectation-maximization (EM) framework that iteratively learns diffusion models from noisy data with arbitrary corruption types.
Our framework employs a plug-and-play Monte Carlo method to accurately estimate clean images from noisy measurements, followed by training the diffusion model using the reconstructed images.
arXiv Detail & Related papers (2024-10-15T03:54:59Z) - Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion [21.939618694037108]
Unsupervised monocular depth estimation has received widespread attention because of its capability to train without ground truth.
We employ a well-converging diffusion model among generative networks for unsupervised monocular depth estimation.
This model significantly enriches the model's capacity for learning and interpreting depth distribution.
arXiv Detail & Related papers (2024-06-14T07:31:20Z) - PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction [5.06136344261226]
CNN-based methods for data fidelity enhancement rely on low-fidelity data patterns and distributions during the training phase.
Our proposed model - Physics-informed Residual Diffusion - demonstrates the capability to elevate the quality of data from both standard low-fidelity inputs.
Experimental results have shown that our approach can effectively reconstruct high-quality outcomes for two-dimensional turbulent flows without requiring retraining.
arXiv Detail & Related papers (2024-04-12T11:45:51Z) - DepthFM: Fast Monocular Depth Estimation with Flow Matching [22.206355073676082]
Current discriminative depth estimation methods often produce blurry artifacts, while generative approaches suffer from slow sampling due to curvatures in the noise-to-depth transport.
Our method addresses these challenges by framing depth estimation as a direct transport between image and depth distributions.
Our approach achieves competitive zero-shot performance on standard benchmarks of complex natural scenes while improving sampling efficiency and only requiring minimal synthetic data for training.
arXiv Detail & Related papers (2024-03-20T17:51:53Z) - Adaptive Training Meets Progressive Scaling: Elevating Efficiency in Diffusion Models [52.1809084559048]
We propose a novel two-stage divide-and-conquer training strategy termed TDC Training.
It groups timesteps based on task similarity and difficulty, assigning highly customized denoising models to each group, thereby enhancing the performance of diffusion models.
While two-stage training avoids the need to train each model separately, the total training cost is even lower than training a single unified denoising model.
arXiv Detail & Related papers (2023-12-20T03:32:58Z) - BOOT: Data-free Distillation of Denoising Diffusion Models with
Bootstrapping [64.54271680071373]
Diffusion models have demonstrated excellent potential for generating diverse images.
Knowledge distillation has been recently proposed as a remedy that can reduce the number of inference steps to one or a few.
We present a novel technique called BOOT, that overcomes limitations with an efficient data-free distillation algorithm.
arXiv Detail & Related papers (2023-06-08T20:30:55Z) - Low-Light Image Enhancement with Wavelet-based Diffusion Models [50.632343822790006]
Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration.
We propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
arXiv Detail & Related papers (2023-06-01T03:08:28Z) - CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion
Models [72.93652777646233]
Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings.
We propose a new paradigm that treats COD as a conditional mask-generation task leveraging diffusion models.
Our method, dubbed CamoDiffusion, employs the denoising process of diffusion models to iteratively reduce the noise of the mask.
arXiv Detail & Related papers (2023-05-29T07:49:44Z) - How Much is Enough? A Study on Diffusion Times in Score-based Generative
Models [76.76860707897413]
Current best practice advocates for a large T to ensure that the forward dynamics brings the diffusion sufficiently close to a known and simple noise distribution.
We show how an auxiliary model can be used to bridge the gap between the ideal and the simulated forward dynamics, followed by a standard reverse diffusion process.
arXiv Detail & Related papers (2022-06-10T15:09:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.