Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision

TL;DR

This paper introduces a differentiable rendering method for implicit 3D shape and texture representations, enabling learning from RGB images without 3D supervision, and producing high-quality reconstructions and watertight meshes.

Contribution

It presents a novel differentiable rendering formulation for implicit representations, allowing direct learning from RGB images without requiring 3D supervision.

Findings

Single-view reconstructions rival fully supervised methods

Method can produce watertight meshes from multi-view data

Implicit differentiation enables analytical depth gradient computation

Abstract

Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differentiable rendering techniques to train reconstruction models from RGB images. Unfortunately, these approaches are currently restricted to voxel- and mesh-based representations, suffering from discretization or low resolution. In this work, we propose a differentiable rendering formulation for implicit shape and texture representations. Implicit representations have recently gained popularity as they represent shape and texture continuously. Our key insight is that depth gradients can be derived analytically using the concept of implicit differentiation. This allows us to learn implicit shape and texture representations directly from RGB images. We experimentally show that our single-view reconstructions rival those learned with full 3D supervision. Moreover, we find that our method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes.