MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond (2309.16553v1)

Abstract: Neural radiance fields (NeRF) and its subsequent variants have led to remarkable progress in neural rendering. While most of recent neural rendering works focus on objects and small-scale scenes, developing neural rendering methods for city-scale scenes is of great potential in many real-world applications. However, this line of research is impeded by the absence of a comprehensive and high-quality dataset, yet collecting such a dataset over real city-scale scenes is costly, sensitive, and technically difficult. To this end, we build a large-scale, comprehensive, and high-quality synthetic dataset for city-scale neural rendering researches. Leveraging the Unreal Engine 5 City Sample project, we develop a pipeline to easily collect aerial and street city views, accompanied by ground-truth camera poses and a range of additional data modalities. Flexible controls over environmental factors like light, weather, human and car crowd are also available in our pipeline, supporting the need of various tasks covering city-scale neural rendering and beyond. The resulting pilot dataset, MatrixCity, contains 67k aerial images and 452k street images from two city maps of total size $28km^2$. On top of MatrixCity, a thorough benchmark is also conducted, which not only reveals unique challenges of the task of city-scale neural rendering, but also highlights potential improvements for future works. The dataset and code will be publicly available at our project page: https://city-super.github.io/matrixcity/.

• The paper introduces MatrixCity, a synthetic dataset capturing 67k aerial and 452k street images to advance urban neural rendering methods.
• It details a novel pipeline using Unreal Engine 5’s City Sample project to generate high-fidelity visuals and diverse rendering properties like depth maps and normals.
• Benchmark results show that while grid-based models work well on aerial data, they struggle with detailed street views, highlighting areas for future algorithm improvements.

An Expert Analysis of MatrixCity: A Large-scale Dataset for City-Scale Neural Rendering

The paper "MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond" introduces a synthetic dataset specifically designed to support advanced research in city-scale neural rendering. This work addresses a critical gap within the field of neural rendering: the lack of comprehensive and high-quality data suitable for rendering at an urban scale. Traditional datasets have largely confined themselves to smaller scenes or have been limited by privacy and logistical constraints inherent in real-world data collection. The authors leverage the power of Unreal Engine 5 to create MatrixCity, a dataset that encapsulates the breadth and complexity of urban environments efficiently and at scale.

Dataset Construction and Features

MatrixCity is built using a novel data collection pipeline that utilizes the Unreal Engine 5's City Sample project. This tool enables researchers to systematically capture a diverse array of urban scenes, including aerial and street-level views. The small city in MatrixCity offers a data density with 67k aerial and 452k street images spread over 28 km². Key strengths of MatrixCity are its high-quality visuals and the variety of data it provides, including depth maps, normals, and other rendering properties. The dataset is coupled with dynamic environmental controls, like lighting and weather variations, that add to the realism and applicability of the dataset.

Through its flexibility, MatrixCity facilitates the exploration of varied research paths beyond basic neural rendering tasks, addressing neural rendering, depth estimation, and inverse rendering.

Evaluation and Benchmarking

An important component of the paper is the extensive benchmarking performed on the dataset using state-of-the-art neural rendering methods such as NeRF, DVGO, TensoRF, Instant-NGP, and MipNeRF-360. The challenges identified include integrating aerial and street data and modeling detailed urban environments with consistent quality. The experimental setup highlights the challenges linked to rendering dense urban scenes, particularly the difficulty of synthesizing accurate textures and details in large-scale environments with existing models.

The paper observes that grid-based methods like MipNeRF-360 and Instant-NGP perform competently on aerial data but struggle as the detail complexity increases in street-view scenarios. This underscores potential improvements needed in neural rendering algorithms to effectively deal with real-world dynamic scenarios.

Implications for Future Research

The MatrixCity dataset is a significant contribution to the field, offering a nuanced platform to evaluate and improve rendering systems. The provided benchmark results and analytic insights can guide algorithmic development to better capture the complexities of urban environments. MatrixCity opens research avenues in real-world applications such as autonomous navigation, urban planning, and augmented reality experiences.

One future trajectory could be the synthesis of more specialized datasets from MatrixCity, tailored towards use cases in dynamic lighting and scenario modeling, possibly incorporating more complex camera motions or additional environmental factors. Researchers might also explore hybrid approaches that combine existing model advantages to enhance performance and robustness in city-scale scenarios.

In sum, MatrixCity represents a promising advance in city-scale neural rendering and a valuable resource for researchers aiming to push the boundaries of what neural rendering can achieve in replicating real-world environments at scale.