对比隐式与显式三维重建方法，发现外观嵌入不提升几何精度。
On the Geometric Accuracy of Implicit and Primitive-based Representations Derived from View Rendering Constraints
- 基于视角渲染约束，系统比较隐式与显式方法的几何表现
- 外观嵌入仅减少显式方法所需图元数，未改善几何准确率
- 凸面点云表示更紧凑清晰，适合空间机器人避障等安全场景
我们首次系统比较了基于空间的3D物体重建中隐式与显式新视角合成方法，评估外观嵌入的作用。尽管嵌入能通过建模光照变化提升图像保真度，但我们发现其并未带来几何精度的实质性提升——这对空间机器人应用至关重要。基于SPEED+数据集，对比K-Planes、高斯点阵与凸面点阵方法，结果表明嵌入主要降低显式方法所需图元数量，而非增强几何保真度。此外，凸面点阵生成的表示更紧凑、更少杂乱，对交互与碰撞避免等安全关键任务具有优势。研究澄清了外观嵌入在几何导向任务中的局限性，并揭示了空间场景下重建质量与表示效率间的权衡。
原文摘要 · Abstract (English)
We present the first systematic comparison of implicit and explicit Novel View Synthesis methods for space-based 3D object reconstruction, evaluating the role of appearance embeddings. While embeddings improve photometric fidelity by modeling lighting variation, we show they do not translate into meaningful gains in geometric accuracy - a critical requirement for space robotics applications. Using the SPEED+ dataset, we compare K-Planes, Gaussian Splatting, and Convex Splatting, and demonstrate that embeddings primarily reduce the number of primitives needed for explicit methods rather than enhancing geometric fidelity. Moreover, convex splatting achieves more compact and clutter-free representations than Gaussian splatting, offering advantages for safety-critical applications such as interaction and collision avoidance. Our findings clarify the limits of appearance embeddings for geometry-centric tasks and highlight trade-offs between reconstruction quality and representation efficiency in space scenarios.
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