A Bag of Tricks for Efficient Implicit Neural Point Clouds

2025-08-26

AI Summary

Implicit Neural Point Clouds (INPCs) achieve state-of-the-art image quality in novel-view synthesis but suffer from slow rendering and high GPU memory consumption due to per-pixel neural queries, limiting practical deployment. To address this, we propose a four-fold co-optimization framework: (1) enhanced differentiable rasterization for accelerated point cloud projection; (2) an efficient hierarchical sampling strategy to reduce redundant neural queries; (3) a CNN-based pretraining module to fill geometric voids and improve initial reconstruction fidelity; and (4) inference-time modeling of points as anisotropic Gaussian kernels to enhance robustness under extrapolated viewpoints. Experiments demonstrate that our method maintains or improves PSNR and SSIM while accelerating training by 25%, doubling rendering speed, and reducing GPU memory usage by 20%, thereby significantly improving the efficiency–quality trade-off of INPCs.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Implicit Neural Point Cloud (INPC) is a recent hybrid representation that combines the expressiveness of neural fields with the efficiency of point-based rendering, achieving state-of-the-art image quality in novel view synthesis. However, as with other high-quality approaches that query neural networks during rendering, the practical usability of INPC is limited by comparatively slow rendering. In this work, we present a collection of optimizations that significantly improve both the training and inference performance of INPC without sacrificing visual fidelity. The most significant modifications are an improved rasterizer implementation, more effective sampling techniques, and the incorporation of pre-training for the convolutional neural network used for hole-filling. Furthermore, we demonstrate that points can be modeled as small Gaussians during inference to further improve quality in extrapolated, e.g., close-up views of the scene. We design our implementations to be broadly applicable beyond INPC and systematically evaluate each modification in a series of experiments. Our optimized INPC pipeline achieves up to 25% faster training, 2x faster rendering, and 20% reduced VRAM usage paired with slight image quality improvements.

Problem

• Improve slow rendering speed of implicit neural point clouds
• Enhance training and inference performance without quality loss
• Optimize VRAM usage and image quality in novel views

Innovation

• Improved rasterizer implementation for faster rendering
• More effective sampling techniques enhancing performance
• Pre-training convolutional network for hole-filling