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Optical Flow with RAFT: Part 1

Dive into Deep Learning for Optical Flow

Photo by Zdeněk Macháček on Unsplash

In this post we will learn about a flagship deep learning approach to Optical Flow that won the 2020 ECCV best paper award and has been cited over 1000 times. It is also the basis for many top performing models on the KITTI benchmark. This model is called RAFT: Recurrent All-Pairs Field Transforms for Optical Flow and is readily available in PyTorch or on GitHub. The implementations make it highly accessible, but the model is complex and understanding it can be confusing. In this post we will break down RAFT into its basic components and learn about each of them in detail. Then we will learn how to use it in Python to estimate optical flow. In part 2 we will explore the obscure details and visualize the different blocks so we can gain deeper intuition for how they work.

IntroductionFoundations of RAFTVisual SimilarityIterative UpdatesHow to use RAFTConclusion

Introduction

Optical Flow

Optical flow is the apparent motion of pixels in a sequence of images. In order for optical flow to be estimated, the movement of an object in a scene must have a corresponding brightness displacement. This means that a moving red ball in one image should have the same brightness and color in the next image, this enables us to determine how much it has moved in terms of pixels. Figure 1 shows an example of Optical Flow where a ceiling fan rotating counter-clock wise is captured by a sequence of images.

Figure 1. Optical Flow Estimation for a sequence of images. Frame 1, Frame 2, Computed Optical Flow between frame 1 and frame 2. Source: Author.

The color image on the far right contains the apparent motion of every pixel from frame 1 to frame 2, it is color codes such that different colors indicate different horizontal and vertical directions of pixel motion. This is an example of Dense Optical Flow estimation.

An estimation of Dense Optical Flow assigns each pixel a 2D flow vector describing it’s horizontal and vertical displacement over a time interval. In Sparse Optical Flow this vector is only assigned to pixels that correspond to strong features such as corners and edges. In order for a flow vector to exist, the pixel must have the same intensity at time t as it does at time t+1, this is known as the brightness consistency assumption. The image intensity or brightness at location (x,y) at time t is given by I(x,y,t). Let’s visualize this with an example of known pixel displacement below in figure 2, where dx and dy are the horizontal and vertical image displacements and dt is the time difference between frames.

Figure 2. Displacement of a single pixel from time t to t+dt. The brightness consistency assumption implies that this pixel is the same color and intensity in both frames. Source: Author.The brightness consistency assumption implies that a pixel at (x,y,t) will have the same intensity at (x+dx, y+dy, t+dy). Therefore: I(x, y, t) = I(x+dx, y+dy, t+dt).

From the brightness consistency assumption we can derive the Optical Flow equation by expanding the right hand side with a 1ˢᵗ Order Taylor Approximation about (x, y, t) [1].

Optical Flow equation derivation. Source: Author.

The horizontal and vertical gradients, Iₓ and Iᵧ, can be approximated with the Sobel Operator and the time gradient Iₜ is known since we have images at time t and t+1. The flow equation has two unknowns u and v which are the horizontal and vertical displacements over time dt. The two unknowns in a single equation make this is an underdetermined problem and many attempts have been made to solve for u and v. RAFT is a deep learning approach to estimating u and v, but it is actually more involved than just predicting flow based on two frames. It was meticulously designed to accurately estimate optical flow fields , in the next section we will dive into it’s intricate details.

Foundations of RAFT

RAFT is a Deep Neural Network that is able to estimate the Dense Optical Flow given a pair of sequential images I₁ and I₂. It estimates a flow displacement field (, ) that maps each pixel (u, v) in I₁ to its corresponding pixel (u’, v’) in I₂, where (u’, v’) = (u + (u), v + (v)). It works by extracting features, finding their correlations, and then iteratively updates the flow in a manner that mimics an optimization algorithm. The initial flow is either initialized as all 0’s or it can be initialized with the forward projected previous flow estimate which is known as a warm start. The overall architecture is shown below.

Figure 3. Architecture of RAFT. Modified from Source.

Notice how it has three main blocks: Feature Encoder Block, Visual Similarity Block, and an Iterative Update Block. The RAFT architecture comes in two sizes a large with 4.8 million parameters and a small with 1 million parameters, in this post we will focus on the large architecture, but understanding the small architecture will be of little account once we understand the large one.

Feature Extraction

RAFT performs feature extraction on both input images using a Convolutional Neural Network (CNN) that consists of six residual blocks and downsamples each image to 1/8 resolution with D feature maps.

Figure 4. Encoding Block of RAFT. Modified from Source.

The feature encoder network g operates on both images with shared weights, while the context encoder network f only operates on I₁ and extracts features that serve as a primary reference for the flow estimation. Aside from minor differences, the overall architecture of both networks is nearly the same. The context network uses batch normalization while the feature network uses instance normalization, and the context network extracts C = c + h feature maps, where c is the number of context feature maps and h is the number of hidden feature maps that will initialize the hidden state of the Iterative Update Block.

Function mappings for the feature network f and the context network g. Source: Author.NOTE: The original paper constantly refers to feature map sizes H/8xW/8 with the shorthand notation: HxW. This can be confusing, so we will follow the convention of H’ = H/8 such that a feature map size is H’xW’. We also refer to the feature map tensor extracted from I₁ as g¹, likewise for I₂.

Visual Similarity

Correlation Volume

The Visual Similarity is a 4D H’xW’xH’xW’ All-Pairs Correlation Volume C that is computed by taking the dot product of the feature maps.

Computation of the 4D correlation Volume. Modified from Source.

In the correlation volume, each pixel from feature map has a computed correlation with every pixel from feature map, we call each of these correlations a 2D response map (see figure 5). It can be challenging to think in 4D, so imagine flattening the first two dimensions of the volume: (H’xW’)xH’xW’, we now have a 3D volume where each pixel from has it’s own 2D response map that shows it’s correlation to each pixel location of . Since features are derived from images, the response maps actually indicate how much a given pixel of I₁ is correlated to each pixel of I₂.

The Visual Similarity is an all-pairs Correlation Volume that relates the pixels of I₁ to every single pixel of I₂ by computing the correlation of every feature map at each pixel location

Correlation Pyramid

The correlation volume effectively provides information for small pixel displacements, but will likely struggle to capture larger displacements. In order to capture both large and small pixel displacements, multiple levels of correlation are needed. To solve this, we construct a Correlation Pyramid which contains multiple levels of correlation volumes where different levels of correlation volumes are produced by average pooling the last two dimensions of the correlation volume. The average pooling operation produces coarse I₂ correlation features in the last two dimensions of the volume, this enables the fine features of I₁ to be correlated with the progressively coarse features of I₂. Each pyramid level contains smaller and smaller 2D response maps.

Figure 5. Left: Relationship of a single pixel in I₁ to all pixels of I₂. Right: 2D response maps of various correlation volumes in the correlation pyramid. Source.

Figure 5 shows the different 2D response maps for different levels of average pooling. The dimensions of the corresponding correlation volumes are stacked together into a 5D Correlation Pyramid which contains four levels with kernel sizes: 1, 2, 4, and 8. The pyramid provides robust information about both large and small displacements while maintaining a high resolution with respect to I₁.

Correlation Lookup

The Correlation Lookup Operator L꜀ generates new feature maps by indexing features from the correlation pyramid at each level. Given the current Optical Flow estimate (, ), each pixel of I₁: x = (u, v) is mapped to it’s estimated correspondence in I₂: x’ = (u + f¹(u) + v + f²(v)). We define a local neighborhood around x’:

Neighborhood of radius r around pixel x’ = (u’, v’). Source: Author.Correspondence is the new location of a pixel in I₂ based on it’s flow estimate

A constant radius across all pyramid levels means that a larger context will be incorporated across the lower levels. i.e. a radius of 4 corresponds to 256 pixels at the original resolution.

In practice this neighborhood is a square grid centered around each fine resolution pixel, with r = 4 we get a 9×9 grid around each pixel, where each dimension is of length (2r + 1). We obtain new feature maps by bilinearly resampling the correlation features around each pixel at locations defined by the grid (edge locations are zero padded). Due to the flow offsets and average pooling, the neighborhood grid values will likely be floating points, the bilinear resampling readily handles this by taking weighted average of the 2×2 sub-neighborhood of nearby pixels. In other words, the resampling will give us subpixel accuracy. We resample at all pixel locations in each layer of the pyramid, this can be efficiently done with F.grid_sample() from PyTorch. These resampled features are known as the Correlation Features and they are input into the Update Block.

Efficient Correlation Lookup (Optional)

The correlation lookup scales with O(N²) where N is the number of pixels, this could be a bottleneck for large images but there is an equivalent operation that scales with O(NM) where M is the number of pyramid levels. This operations combines the correlation pyramid with the lookup and operation exploits the linearity of the inner product and average pooling. The average correlation response Cᵐ (pyramid level m) over a 2ᵐx2ᵐ grid is shown below.

Equivalent Correlation implementation. Source.

For a given pyramid level m we don’t need to sum over the feature map , this means that the correlation can be computed by taking the inner product of feature map with the average pooled feature map , this has a complexity of O(N). Since this is only valid for a single Pyramid level m, we must compute this inner product for each level, making it scale by O(M) for a total complexity of O(NM). Instead of precomputing the correlations for the pyramid, we only precompute the pooled feature maps and compute the correlation values on demand when the lookup occurs.

Iterative Updates

The update operator estimates a series of flows: {f₀, f₁ ,…, f} from an initial starting point f₀, which can either be all 0’s or the forward projected previous flow estimation (warm start). In each iteration k it produces a flow update direction Δf which is added to the current estimate: fₖ₊₁ = fₖ + Δfₖ. The update operator mimics an optimization algorithm and is trained to provide updates such that the estimated flow sequence converges to a fixed point: fₖ → f*.

Update Block

The update block takes as inputs: the correlation features, current flow estimate, context features, and the hidden features. Its architecture with highlighted sub-blocks is displayed below.

Figure 6. RAFT Update Block for large architecture with different sub-blocks highlighted. Blue-Feature extraction block, Red — Recurrent Update Block, Green — Flow Head. Modified from Source.

The sub-blocks inside the Update Block are:

Feature Extraction Block — extracts motion features from the correlation, flow, and I₁ (context network).Recurrent Update Block — Recurrently computes flow updatesFlow Head — Final convolutional layers that re-size flow estimate to H/8 x W/8 x 2

As seen in figure 6, the input to the Recurrent Update Block is the concatenation of the flow, correlation, and context features. The latent hidden state is initialized with the hidden features from the context network. (The context network extracts a stack of 2D feature maps that is then separated into the context and the hidden feature maps). The Recurrent Update Block consists of 2 separable ConvGRUs that enable an increased receptive field without significantly increasing the network size. On each update, the hidden state from the Recurrent Update Block is passed to the Flow Head to obtain a flow estimate of size H/8 x W/8 x 2. This estimate is then upsampled using Convex Upsampling.

Convex Upsampling

The authors of RAFT experimented with both bilinear and convex upsampling and found that convex upsampling provides a significant performance boost.

Figure 7. Comparison of Bilinear VS Convex Upsampling. Source.Convex Upsampling estimates each fine pixel as the convex combination of it’s neighboring 3×3 grid of coarse pixels

Let’s break down how Convex Upsampling works, figure 8 below provides a nice visual.

Figure 8. Convex Upsampling example at a single full res pixel (purple). Source.

First, we assume that a fine resolution pixel is the convex combination of a 3×3 grid of it’s nearest coarse neighbors. This assumption implies that the weighed sum of the coarse pixels must equal the true fine resolution pixel, with the constraint that the weights sum to one and are non-negative. Since we are upsampling by a factor of eight, each coarse pixel must be broken down into 64 (8×8) fine pixels (the visual in figure 8 is not to scale). We also notice that each of the 64 pixels in the center of the 3×3 grid will need it’s own set of weights, making the total number of weights required: (H/8 x W/8 x (8x8x9)).

In practice, the weights are parameterized with a neural network, the convex upsampling block uses two convolutional layers to predict a (H/8 x W/8 x (8x8x9)) mask and then takes a softmax over the weights of the nine neighbors leaving a mask of shape (H/8 x W/8 x (8×8)). We then use this mask to obtain a weighted combination over the neighborhood and reshape to get a HxWx2 flow field.

Training

The objective function for RAFT is able to capture all iterative flow predictions. Formally, it is the sum of weighted l1 distances between the flow predictions and ground truth, with exponentially increasing weights.

Loss for RAFT, γ = 0.8. Source.

How to use RAFT

We can use RAFT to estimate Dense Optical Flow on our own images. First we will need to clone the GitHub repository and download the models. Code for this tutorial is on GitHub.

!git clone https://github.com/princeton-vl/RAFT.git

%cd RAFT
!./download_models.sh
%cd ..

The pre-trained RAFT models come in a few different flavors, according to the authors they are:

raft-chairs — trained on FlyingChairsraft-things — trained on FlyingChairs + FlyingThingsraft-sintel — trained on FlyingChairs + FlyingThings + Sintel + KITTI (model used for submission)raft-kitti — raft-sintel finetuned on only KITTIraft-small — trained on FlyingChairs + FlyingThings

Next we add the core of RAFT to the path

sys.path.append(‘RAFT/core’)

Now, we need some helper functions to interface with the RAFT class. NOTE: these helpers are written for CUDA only, but you can easily access a GPU with Colab.

import torch
from raft import RAFT
from utils import flow_viz
from utils.utils import InputPadder

def process_img(img, device=’cuda’):
return torch.from_numpy(img).permute(2, 0, 1).float()[None].to(device)

def load_model(weights_path, args):
“”” Loads model to CUDA only “””
model = RAFT(args)
pretrained_weights = torch.load(weights_path, map_location=torch.device(“cpu”))
model = torch.nn.DataParallel(model)
model.load_state_dict(pretrained_weights)
model.to(“cuda”)
return model

def inference(model, frame1, frame2, device=’cuda’, pad_mode=’sintel’,
iters=12, flow_init=None, upsample=True, test_mode=True):

model.eval()
with torch.no_grad():
# preprocess
frame1 = process_img(frame1, device)
frame2 = process_img(frame2, device)

padder = InputPadder(frame1.shape, mode=pad_mode)
frame1, frame2 = padder.pad(frame1, frame2)

# predict flow
if test_mode:
flow_low, flow_up = model(frame1,
frame2,
iters=iters,
flow_init=flow_init,
upsample=upsample,
test_mode=test_mode)
return flow_low, flow_up

else:
flow_iters = model(frame1,
frame2,
iters=iters,
flow_init=flow_init,
upsample=upsample,
test_mode=test_mode)

return flow_iters

def get_viz(flo):
flo = flo[0].permute(1,2,0).cpu().numpy()
return flow_viz.flow_to_image(flo)

Notice the input padding in inference(), we will need to ensure that all images are divisible by 8. The raft.py code can easily be accessed from the command line, but if we want to interface with it, we will need to rewrite some of it, or we can make a special class to pass arguments to it.

# class to interface with RAFT
class Args():
def __init__(self, model=”, path=”, small=False,
mixed_precision=True, alternate_corr=False):
self.model = model
self.path = path
self.small = small
self.mixed_precision = mixed_precision
self.alternate_corr = alternate_corr

“”” Sketchy hack to pretend to iterate through the class objects “””
def __iter__(self):
return self

def __next__(self):
raise StopIteration

The default initialization of the Args class will interface directly with any of the large RAFT models. To demonstrate RAFT, we will use frames from a video of a slowly rotating ceiling fan. Now we can load a model and estimate the optical flow.

model = load_model(“RAFT/models/raft-sintel.pth”, args=Args())
flow_low, flow_up = inference(model, frame1, frame2, device=’cuda’, test_mode=True)

Test mode will return both the 1/8 res flow along with the Convex Upsampled flow.

Figure 9. Top: Input image sequence for RAFT. Bottom: 1/8 res and upsampled Optical Flow estimates. Images are from the Author. Source: Author.

Once again, we can see the significant benefit of convex upsampling, now let’s view the advantages of extra iterations. Figure 10 shows a gif of 20 iterations on the ceiling fan images.

flow_iters = inference(model, frame1, frame2, device=’cuda’, pad_mode=None, iters=20, test_mode=False)Figure 10. Progressive Iterations of Optical Flow Estimation. Source: Author.

We can see a clear benefit from the first few iterations, in this case the model is able to converge in about 10 iterations. Now we will experiment with a warm start, to use a warm initialization, we pass the previous flow estimate at 1/8 resolution to the inference function.

# get previous estimate at 1/8 res
flow_lo, flow_up = inference(model, frame1, frame2, device=’cuda’, pad_mode=None, iters=20, test_mode=True)

# 0 initialization
flow_lo_cold, flow_up_cold = inference(model, frame2, frame3, device=’cuda’, pad_mode=None, flow_init=None, iters=20, test_mode=True)

# warm initialization
flow_lo_warm, flow_up_warm = inference(model, frame2, frame3, device=’cuda’, pad_mode=None, flow_init=flow_lo, iters=20, test_mode=True)Figure 11. Optical flow estimation with 0 VS warm initialization between frames 2 and 3. Source: Author.

In this case we don’t see any improvement, the warm start on the right actually looks worse than the 0 initialized flow. The benefits of warm start seem minimal for this video sequence, but it could be useful for different environments.

Conclusion

In this post we learned about RAFT, an advanced model capable of estimating accurate flow fields. RAFT is able to capture the relationship between all pixels by computing the all-pairs correlation volume from extracted feature maps. The correlation pyramid is constructed to capture both large and small pixel displacements. The look up operator extracts new correlation features from the correlation pyramid based on the current flow estimate. The update block uses the correlation features and the current flow estimate to provide iterative updates that converge to a final flow estimate which is upsampled with convex upsampling. In part 2, we will unpack the network and learn how some of the key blocks work.

References

[1] Horn, B. K. P., & Schunck, B. G. (1981). Determining optical flow. Artificial Intelligence, 17(1–3), 185–203. https://doi.org/10.1016/0004-3702(81)90024-2

[2] Teed, Z., & Deng, J. (2020). Raft: Recurrent all-pairs field transforms for optical flow. Computer Vision — ECCV 2020, 402–419. https://doi.org/10.1007/978-3-030-58536-5_24

Optical Flow with RAFT: Part 1 was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.

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