PERF: Performant, Explicit Radiance Fields
Journal article, 2022

We present a novel way of approaching image-based 3D reconstruction based on radiance fields. The problem of volumetric reconstruction is formulated as a non-linear least-squares problem and solved explicitly without the use of neural networks. This enables the use of solvers with a higher rate of convergence than what is typically used for neural networks, and fewer iterations are required until convergence. The volume is represented using a grid of voxels, with the scene surrounded by a hierarchy of environment maps. This makes it possible to get clean reconstructions of 360° scenes where the foreground and background is separated. A number of synthetic and real scenes from well-known benchmark-suites are successfully reconstructed with quality on par with state-of-the-art methods, but at significantly reduced reconstruction times.

3D reconstruction

neural rendering


non-linear least-squares

computer graphics


Sverker Rasmuson

Embedded Electronics Systems and Computer Graphics

Erik Sintorn

Embedded Electronics Systems and Computer Graphics

Ulf Assarsson

Embedded Electronics Systems and Computer Graphics

Frontiers in Computer Science

26249898 (eISSN)

Vol. 4 871808

Subject Categories

Computational Mathematics

Media Engineering

Computer Vision and Robotics (Autonomous Systems)



More information

Latest update

8/2/2022 1