UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction

TL;DR

UNISURF introduces a unified neural implicit model that combines surface and radiance field representations, enabling mask-free surface reconstruction and high-quality novel view synthesis from multi-view images.

Contribution

The paper presents a novel unified framework that integrates implicit surface and radiance field models, eliminating the need for masks and improving reconstruction quality.

Findings

Outperforms NeRF in surface reconstruction quality.

Achieves mask-free reconstruction comparable to IDR.

Demonstrates effectiveness on multiple datasets.

Abstract

Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing surfaces from multi-view images and synthesizing novel views. Unfortunately, existing methods such as DVR or IDR require accurate per-pixel object masks as supervision. At the same time, neural radiance fields have revolutionized novel view synthesis. However, NeRF's estimated volume density does not admit accurate surface reconstruction. Our key insight is that implicit surface models and radiance fields can be formulated in a unified way, enabling both surface and volume rendering using the same model. This unified perspective enables novel, more efficient sampling procedures and the ability to reconstruct accurate surfaces without input masks. We compare our method on the DTU, BlendedMVS, and a synthetic indoor dataset. Our experiments demonstrate that we outperform NeRF in terms of reconstruction quality while performing on par with IDR without requiring masks.