Abstract Differentiable rendering (DR) enables various computer graphics and computer vision applications through gradient‐based optimization with derivatives of the rendering equation. Most rasterization‐based approaches are built on general‐purpose automatic differentiation (AD) libraries and DR‐specific modules handcrafted using CUDA. Such a system design mixes DR algorithm implementation and algorithm building blocks, resulting in hardware dependency and limited performance. In this paper, we present a practical hardware‐agnostic differentiable renderer called Dressi, which is based on a new full AD design. The DR algorithms of Dressi are fully written in our Vulkan‐based AD for DR, Dressi‐AD, which supports all primitive operations for DR. Dressi‐AD and our inverse UV technique inside it bring hardware independence and acceleration by graphics hardware. Stage packing, our runtime optimization technique, can adapt hardware constraints and efficiently execute complex computational graphs of DR with reactive cache considering the render pass hierarchy of Vulkan. HardSoftRas, our novel rendering process, is designed for inverse rendering with a graphics pipeline. Under the limited functionalities of the graphics pipeline, HardSoftRas can propagate the gradients of pixels from the screen space to far‐range triangle attributes. Our experiments and applications demonstrate that Dressi establishes hardware independence, high‐quality and robust optimization with fast speed, and photorealistic rendering.

Consider the scenario where we are given an ideal functionality of oblivious transfer (OT), and we wish to construct a larger OT by invoking the above functionality as a black box. How many invocations of an ideal OT functionality are necessary? In tackling this problem, some lower bounds were derived using entropy previously. In this paper, we manage to achieve tighter lower bounds by employing a combinatorial approach. This new approach yields lower bounds which are two times larger than the existing bounds.

Linear complexity is an important cryptographic criterion of stream ciphers. The k-error linear complexity of a periodic sequence of period N is defined as the smallest linear complexity that can be obtained by changing k or fewer bits of the sequence per period. This article shows a relationship between the linear complexity and the minimum value k for which the k-error linear complexity is strictly less than the linear complexity.