# Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency

## TL;DR

This paper introduces a differentiable ray consistency framework that enables the use of multi-view observations as supervision for improving single-view 3D shape reconstruction.

## Contribution

It proposes a novel differentiable formulation of view consistency called differentiable ray consistency (DRC) for single-view 3D reconstruction.

## Key findings

- Improves 3D reconstruction accuracy over existing methods.
- Effectively leverages various multi-view observations as supervision.
- Demonstrates success on PASCAL VOC dataset.

## Abstract

We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view. We do so by reformulating view consistency using a differentiable ray consistency (DRC) term. We show that this formulation can be incorporated in a learning framework to leverage different types of multi-view observations e.g. foreground masks, depth, color images, semantics etc. as supervision for learning single-view 3D prediction. We present empirical analysis of our technique in a controlled setting. We also show that this approach allows us to improve over existing techniques for single-view reconstruction of objects from the PASCAL VOC dataset.