Abstract Tessellation is critical for real‐time rendering of Catmull‐Clark subdivision (CCS) surfaces, with control mesh singularities posing a major challenge. Prior works typically pre‐subdivide singular regions to a fixed depth offline to facilitate efficient runtime tessellation, yet this practice fails to meet view‐dependent error requirements for high‐fidelity rendering—especially at pixel‐level accuracy. Hence, we present EBOAT, a novel direct‐evaluation tessellation method that integrates memory‐efficient feature‐adaptive subdivision via a connectivity‐elided representation for singularities, along with a two‐phase error control strategy and improved subdivision depth estimation built upon it. With a uniform tessellation level of 64, EBOAT achieves a 3.8×–4.7× speedup over OpenSubdiv, without relying on fixed‐depth pre‐subdivision. Compared to state‐of‐the‐art breadth‐first subdivision, it yields a 4.2×–7.1× speedup while providing more accurate sampling. To further demonstrate its effectiveness, we implement a pixel‐accurate CCS surface rendering pipeline with EBOAT fully integrated, and provide an in‐depth evaluation of rendering quality. Given its advantages in both performance and accuracy, EBOAT offers a promising alternative for real‐time CCS surface rendering in industrial applications.

Watertight tessellation is essential for real-time rendering of large-scale surfaces, particularly for Non-Uniform Rational B-Splines (NURBS) and Catmull-Clark Subdivision (CCS) surfaces. We present WATER, a software-based framework that delivers watertight, non-uniform tessellation with pixel-level accuracy at real-time frame rates. Unlike fixed-function hardware tessellation, WATER adopts a fully GPU-driven pipeline with cache-friendly design and novel algorithms, offering greater flexibility, scalability, and performance. Under our framework, a 2×–3× speedup over hardware tessellation is achieved for bi-3 Bézier surfaces, while bi-7 Bézier surfaces exhibit a 7×–11× improvement. Compared to ETER [Xiong et al. 2023], our method achieves 1.3×–2.1× faster rendering and 52%–72% lower memory usage under the same non-watertight uniform pattern. When enforcing watertightness, a moderate overhead of 23%–51% is incurred. With its advantages in quality, efficiency, and adaptability, WATER provides a compelling alternative for industrial-scale rendering tasks requiring watertightness.

We present ETER, an elastic tessellation framework for rendering large-scale NURBS models with pixel-accurate and crack-free quality at real-time frame rates. We propose a highly parallel adaptive tessellation algorithm to achieve pixel accuracy, measured by the screen space error between the exact surface and its triangulation. To resolve a bottleneck in NURBS rendering, we present a novel evaluation method based on uniform sampling grids and accelerated by GPU Tensor Cores. Compared to evaluation based on hardware tessellation, our method has achieved a significant speedup of 2.9 to 16.2 times depending on the degrees of the patches. We develop an efficient crack-filling algorithm based on conservative rasterization and visibility buffer to fill the tessellation-induced cracks while greatly reducing the jagged effect introduced by conservative rasterization. We integrate all our novel algorithms, implemented in CUDA, into a GPU NURBS rendering pipeline based on Mesh Shaders and hybrid software/hardware rasterization. Our performance data on a commodity GPU show that the rendering pipeline based on ETER is capable of rendering up to 3.7 million patches (0.25 billion tessellated triangles) in real-time (30FPS). With its advantages in performance, scalability, and visual quality in rendering large-scale NURBS models, a real-time tessellation solution based on ETER can be a powerful alternative or even a potential replacement for the existing pre-tessellation solution in CAD systems.

This article reports a survey study (N=1044) exploring the relationship between five metacognitive variables measured in Vandergrift et al. And the participants' listening performance on two listening tests. Results showed that Chinese undergraduates had a fairly high degree of metacognitive awareness of listening in directed attention, problem-solving, and planning and evaluation, but not in person knowledge factor and the mental translation strategy. Subsequent regression analyses indicated that around 13%-15% of the variance in listening performance could be explained by learners' metacognitive awareness in listening. The finding provided further evidence to the relationship between the reported metacognitive awareness in listening and learners' listening performance. Finally, the study identified a differing correlational pattern of MALQ variables concerning Chinese EFL learners, in which mental translation was positively correlated with person knowledge, and negatively correlated with planning/ evaluation and problem-solving. The negative correlations were not reported in previous studies. Theoretical and pedagogical implications of the study are discussed.

This article reports a case study involving four college EFL students in mainland China over a six-month period of self-regulated learning (SRL) in independent settings. The study examined how the met cognition and listening performance of two high-achieving learners and two low-achieving learners may have been affected by the way they engaged with their SRL in extensive listening. It described the learners' engagement during four phases of self-regulated listening, namely task definition, goal setting and planning, strategy enactment, and metacognitive adaptation. Findings revealed differences in the self-regulatory behaviours of the two groups of learners in three SRL phases. Although all the learners defined each listening task in quite similar ways, the two groups differed substantially in their metacognitive engagement in the remaining three phases. The learners' mental representation of their listening process and development through the use of a descriptive metaphor further revealed such qualitative differences. The authors argue that the achievements of the respective learners in listening development were affected by these differences. Theoretical and pedagogical implications of applying a SRL approach for second language (L2) listening development are discussed.