[Paper Review] Fast Differentiable Raycasting for Neural Rendering using Sphere-based Representations

Christoph Lassner|arXiv (Cornell University)|Apr 16, 2020

Computer Graphics and Visualization Techniques4 citations

TL;DR

This paper proposes pulsar, a fast differentiable raycaster that replaces traditional mesh representations with sphere clouds to enable efficient, topology-agnostic rendering and optimization. By leveraging PyTorch integration, it accelerates rendering by orders of magnitude, supporting real-time optimization of scenes with millions of spheres while providing gradients for all scene parameters including position, radius, color, opacity, and camera settings.

ABSTRACT

Differentiable rendering in combination with deep learning promises great advantages: deep learning models can produce realistic scenes rapidly, while differentiable rendering offers consistent scene representations and respective gradients. However, gradient based optimization of classical mesh representations is cumbersome because of the explicit topology encoding. Moreover, complex scenes may need detailed geometric representation, requiring many geometric primitives and a fast rendering operation. We propose to break up the rendering process into multiple parts: (1) the scene representation, (2) a differentiable geometry projection and (3) neural shading. While mature, off-the-shelf models for scene representation and neural shading are widely available, we propose pulsar as a general purpose differentiable geometry engine tightly integrated with PyTorch. By replacing mesh representations with sphere clouds for the scene representation, the operation is fast compared to existing differentiable renderers and avoids problems with surface topology. It provides gradients for the full scene parameterization, i.e., sphere positions, colors, radiuses, opacity and the camera parameters. pulsar can execute many times, up to orders of magnitudes faster than existing renderers and allows real-time rendering and optimization of scenes with millions of spheres. It can be used for 3D reconstruction, rendering and volumetric scene optimization.

Motivation & Objective

• To address the computational inefficiency and topological complexity of mesh-based differentiable rendering in deep learning pipelines.
• To enable real-time optimization of complex 3D scenes with millions of geometric primitives.
• To provide a general-purpose differentiable geometry engine that supports full parameter gradients, including sphere properties and camera parameters.
• To replace computationally heavy mesh-based rendering with a faster, topology-agnostic sphere cloud representation.

Proposed method

• The method replaces explicit mesh topology with a sphere cloud representation, where each sphere is defined by position, radius, color, opacity, and is differentiable.
• It decomposes rendering into three stages: scene representation via sphere clouds, differentiable geometry projection, and neural shading.
• The geometry projection phase performs fast ray-sphere intersection tests using analytical solutions, enabling GPU-accelerated computation.
• All operations are implemented in PyTorch, enabling automatic differentiation across all scene parameters and camera settings.
• The system supports hierarchical and sparse sphere representations to improve performance on large-scale scenes.
• The renderer is designed to be modular and composable, allowing integration with existing neural shading and optimization frameworks.

Experimental results

Research questions

• RQ1 Can a differentiable raycaster based on sphere clouds outperform mesh-based renderers in speed and scalability?
• RQ2 Does a topology-agnostic geometric representation like sphere clouds enable stable and efficient gradient computation across complex scenes?
• RQ3 To what extent can sphere-based rendering achieve real-time optimization for scenes with millions of primitives?
• RQ4 How does the integration of sphere cloud geometry with neural shading improve end-to-end training efficiency?

Key findings

• pulsar achieves rendering speeds orders of magnitude faster than existing differentiable renderers, enabling real-time optimization of scenes with millions of spheres.
• The use of sphere clouds eliminates topological complexity, avoiding issues associated with mesh connectivity and surface representation.
• Full gradients are provided for all scene parameters, including sphere positions, radii, colors, opacities, and camera parameters, enabling end-to-end optimization.
• The system supports efficient, differentiable ray-sphere intersection via analytical solutions, reducing computational overhead.
• The integration with PyTorch allows seamless use in deep learning pipelines for 3D reconstruction, neural rendering, and volumetric optimization.