mesh rasterization

This work addresses the challenge of balancing accuracy and efficiency in real-time novel view synthesis for radiance fields. We propose Radiance Grid—a tetrahedral voxel representation with uniform density, constructed via Delaunay tetrahedralization—where radiance field parameters are defined per tetrahedral cell. Coupled with a Zip-NeRF–style backbone network, the formulation ensures field continuity under topological changes. We design a dedicated rasterizer compatible with ray tracing, enabling hardware-efficient, accurate volume rendering. Compared to existing radiance field methods, Radiance Grid achieves higher rendering speed and fidelity on consumer-grade GPUs. It supports real-time novel view synthesis, fisheye distortion modeling, physics-based simulation, interactive scene editing, and isosurface mesh extraction. Crucially, it is the first approach to achieve efficient, differentiable volume rendering while preserving geometric precision—enabling high-fidelity, real-time applications without sacrificing structural accuracy.

This study addresses the bottleneck of inefficiently converting 3D mesh assets into Neural Radiance Fields (NeRFs) for robotic simulation by proposing a direct conversion pipeline from textured meshes to point-based radiance fields. By sampling mesh geometry and textures to construct ground-truth representations, this method generates neural radiance fields without requiring camera pose sampling or multi-view rendering, thereby substantially simplifying the training process. Experimental results demonstrate that the proposed approach achieves rendering quality comparable to existing baselines while enabling the construction of unified NeRF scenes. These scenes support accurate geometry extraction and collision simulation, effectively accelerating the development of robotic algorithms.

To address geometric distortion and inaccurate recovery of complex textures (e.g., text, portraits) in multi-view image-based 3D mesh reconstruction, this paper proposes a geometry-texture co-optimization framework. First, we enhance the LRM architecture with differentiable Dual Contouring to enable full-resolution geometric supervision. Second, we introduce a rendering-driven NeRF fine-tuning mechanism to improve surface detail modeling. Third, we propose a lightweight, instance-aware texture refinement module—achieving high-fidelity texture recovery in just 4 seconds while preserving feed-forward inference speed. Built upon triplane representation and differentiable rendering, our method achieves a PSNR of 29.79 on the GSO dataset—setting a new state-of-the-art—and significantly improves both 2D image fidelity and 3D geometric accuracy. Moreover, it natively supports text- or image-to-3D generation.

Addressing the challenge of simplifying non-manifold, multiply-connected, and textured triangle meshes, this paper formulates mesh simplification as a 2-dimensional simplicial complex reduction problem—the first such formulation—and introduces a topology-robust edge-collapse framework. Key contributions include: (1) an enhanced quadric error metric adapted to topological changes, ensuring geometric fidelity; (2) a novel texture simplification paradigm that retains only texture colors while decoupling UV layout optimization, thereby eliminating bleeding artifacts entirely; and (3) a color-space-driven texture remapping strategy. The method supports arbitrary topology and level-of-detail (LOD) generation. Comprehensive qualitative and quantitative evaluations, together with user studies, demonstrate consistent superiority over state-of-the-art approaches—significantly improving simplification quality and visual consistency for non-manifold meshes.

This work addresses key limitations in text-to-3D material generation—namely, heavy reliance on large-scale 3D-text paired data, limited editability, and insufficient photorealistic rendering fidelity. We propose an end-to-end framework that operates without 3D-text paired supervision. Our core innovations are threefold: (1) adopting procedural material graphs—not conventional texture maps—as the underlying material representation; (2) designing a segment-wise controlled diffusion model integrated with differentiable rendering to jointly optimize material parameters under text guidance; and (3) enabling fine-grained semantic control via geometric segmentation, text-guided 2D diffusion priors, and material graph parameter initialization. Experiments demonstrate substantial improvements over prior methods in realism, resolution, and interactive editability. The framework supports real-time, high-fidelity material synthesis and flexible, intuitive parameter adjustments—marking a significant step toward controllable, photorealistic text-driven material generation.

本文提出AnyGS2Mesh，一种前馈框架，直接从3D高斯点阵表示中重建3D网格，解决现有方法依赖迭代优化导致的速度慢和分辨率限制问题。

This work proposes an efficient software rasterization method for dense, opaque meshes—comprising hundreds of millions to billions of triangles—as commonly encountered in photogrammetry and related applications, without requiring prebuilt acceleration structures. The approach employs a three-stage CUDA compute shader pipeline: small triangles are processed directly in the first stage using atomicMin operations to record the nearest fragments, while large triangles are deferred to subsequent stages. Compared to Vulkan hardware rasterization, the method achieves 2–5× speedup for single-instance scenes and up to 12× acceleration with instanced rendering, substantially outperforming existing solutions, although it remains approximately an order of magnitude slower on low-polygon-count meshes.

本文提出ExMesh++，通过自适应顶点分割与合并及UV一致性的保持，从多视图图像重建可重照明的UV-PBR网格资产，优化几何、材质和光照。

This study addresses the challenges of low accuracy, high computational cost, and the difficulty of unifying real-time rendering with differentiable optimization in vector graphics rasterization. To this end, this work proposes Windfoil, an algorithm that for the first time treats both tasks as a unified problem. By deriving a closed-form analytical solution for the box-filtered winding number of quadratic Bézier contours, it eliminates the need for approximate sampling and enables efficient, GPU-friendly computation. Experimental results demonstrate that Windfoil surpasses Skia and Slug in rendering fidelity while maintaining comparable performance. Furthermore, in differentiable optimization tasks, it achieves equivalent or superior reconstruction quality with substantially reduced per-step computational costs, thereby supporting interactive rendering of scenes comprising tens of thousands of shapes.

Existing grid-based differentiable rendering methods struggle to scale to large, unbounded scenes and rely on custom renderers. This work proposes a novel differentiable mesh rendering framework that dynamically extracts geometry from signed distance functions using nested mesh shells and synthesizes images via standard triangle rasterization combined with alpha compositing. Notably, this approach is the first to achieve compatibility with off-the-shelf, non-differentiable mesh renderers without requiring explicit gradients with respect to vertex positions, thereby overcoming the scene-scale limitations inherent in conventional mesh-based methods. Experiments demonstrate that the method matches the performance of state-of-the-art surface rendering techniques on object-centric scenes and achieves near state-of-the-art novel view synthesis quality on unbounded real-world scenes compared to current non-mesh-based approaches.