# Learning Direct Optimization for Scene Understanding

## TL;DR

This paper introduces LiDO, a learning-based method that directly predicts updates to scene parameters for better image explanation, outperforming traditional error minimization approaches in speed and accuracy.

## Contribution

LiDO is a novel approach that trains a network to directly predict scene parameter updates, improving convergence and robustness in scene understanding tasks.

## Key findings

- LiDO converges faster than error-based optimization methods.
- LiDO produces more accurate scene explanations.
- LiDO generalizes well from synthetic to real images.

## Abstract

We develop a Learning Direct Optimization (LiDO) method for the refinement of a latent variable model that describes input image x. Our goal is to explain a single image x with an interpretable 3D computer graphics model having scene graph latent variables z (such as object appearance, camera position). Given a current estimate of z we can render a prediction of the image g(z), which can be compared to the image x. The standard way to proceed is then to measure the error E(x, g(z)) between the two, and use an optimizer to minimize the error. However, it is unknown which error measure E would be most effective for simultaneously addressing issues such as misaligned objects, occlusions, textures, etc. In contrast, the LiDO approach trains a Prediction Network to predict an update directly to correct z, rather than minimizing the error with respect to z. Experiments show that our LiDO method converges rapidly as it does not need to perform a search on the error landscape, produces better solutions than error-based competitors, and is able to handle the mismatch between the data and the fitted scene model. We apply LiDO to a realistic synthetic dataset, and show that the method also transfers to work well with real images.