Topology optimization has found significant utility in additive manufacturing, but the resulting geometries are often difficult to realize using subtractive methods. The complex, curved surfaces of optimized designs typically require time-consuming point milling operations. In contrast, geometries composed of developable surfaces are well suited to flank milling, enabling substantial improvements in machining efficiency and surface quality. However, globally minimizing Gaussian curvature leads to overly simplistic designs. We address this challenge with a novel method that represents geometry using three implicit neural fields, optimized to produce machinable components. The resulting designs consist of regions of developable surfaces separated by seamlines where curvature is concentrated. Seamlines are efficiently computed as intersections of the implicit fields, while the geometry emerges through their product. By constraining surfaces to align with the cutter’s normal or tangential direction, our approach further accommodates practical constraints of 3-axis machining. We demonstrate concurrent optimization of topology and Gaussian curvature, enabling control over seamline feature scale. Compared to methods that enforce subtractive machinability without considering milling efficiency, our approach consistently reduces machining time while maintaining competitive structural performance.
We present Radiance Cascade Path Guiding (RCPG), a real-time product path guiding method that improves upon previous state-of-the-art real-time path guiding methods both in quality and performance. We derive bounds for radiance intervals such that the direction of incoming light is not misrepresented by radiance cascades. This allows us to take advantage of the hierarchical nature of radiance cascades in a sampling process that takes the product of the incoming radiance and the material’s BSDF into account. It is also possible to compute the probability distribution function of RCPG sampling for any given direction, making it readily compatible with other sampling methods, such as multiple importance sampling (MIS) and unbiased ReSTIR. As a product sampling scheme, RCPG can use a single sampling method for producing scattering directions, as opposed to needing separate importance sampling schemes for incident radiance and the BSDF. Compared to prior state-of-the-art real-time path guiding methods utilizing multi-sample MIS, this allows RCPG to generally trace twice as many paths per pixel within a real-time frame rate. Because the data structure is defined in world space, it can be used to generate samples starting from off-screen path vertices, which is necessary for effective multi-bounce path guiding.
Document image rectification aims to restore images degraded by geometric distortions. Existing U-shaped encoder-decoder methods rely on hierarchical upsampling operations to recover dense flow fields, which provide limited control over the displacement at individual grid positions, leading to spatial inconsistency and poor detail restoration. To address this issue, we propose Q‑DRFormer, a novel framework that formulates rectification as a set prediction task. Specifically, we design a set of queries, where each query corresponds to a position in the flow field grid and is responsible for regressing a 2D anchor point that indicates the sampling position in the distorted input. This one-to-one design establishes a deterministic query-position mapping, enabling fine-grained control and improving the spatial consistency of the predicted flow field. Exploiting this deterministic correspondence, we further propose a progressive flow refinement strategy that progressively refines anchor points through layer-wise offset prediction to enhance fine-grained restoration. Moreover, considering the lack of downstream-task-oriented evaluation in existing studies, we apply the rectification method to scene table detection and contribute a real-world test set along with corresponding evaluation metrics to fill this gap. Extensive experiments demonstrate that Q-DRFormer outperforms existing methods. The code is available at https://github.com/vvvvvair/Q-DRFormer.
This is an erratum for the article “Guidestar-Free Adaptive Optics with Asymmetric Apertures” published in ACM Trans. Graph. 45, 5, Article 39 (June 2026), 17 pages.
This is an erratum for the article “Robust and Efficient Penetration-Free Elastodynamics without Barriers” published in ACM Trans. Graph. 45, 5, Article 40 (June 2026), 20 pages.
