OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving

With deep learning and computer vision technology development, autonomous driving provides new solutions to improve traffic safety and efficiency. The importance of building high-quality datasets is self-evident, especially with the rise of end-to-end autonomous driving algorithms in recent years. Data plays a core role in the algorithm closed-loop system. However, collecting real-world data is expensive, time-consuming, and unsafe. With the development of implicit rendering technology and in-depth research on using generative models to produce data at scale, we propose OASim, an open and adaptive simulator and autonomous driving data generator based on implicit neural rendering. It has the following characteristics: (1) High-quality scene reconstruction through neural implicit surface reconstruction technology. (2) Trajectory editing of the ego vehicle and participating vehicles. (3) Rich vehicle model library that can be freely selected and inserted into the scene. (4) Rich sensors model library where you can select specified sensors to generate data. (5) A highly customizable data generation system can generate data according to user needs. We demonstrate the high quality and fidelity of the generated data through perception performance evaluation on the Carla simulator and real-world data acquisition. Code is available at https://github.com/PJLab-ADG/OASim.

StreetSurf: Extending Multi-view Implicit Surface Reconstruction to Street Views

We present a novel multi-view implicit surface reconstruction technique, termed StreetSurf, that is readily applicable to street view images in widely-used autonomous driving datasets, such as Waymo-perception sequences, without necessarily requiring LiDAR data. As neural rendering research expands rapidly, its integration into street views has started to draw interests. Existing approaches on street views either mainly focus on novel view synthesis with little exploration of the scene geometry, or rely heavily on dense LiDAR data when investigating reconstruction. Neither of them investigates multi-view implicit surface reconstruction, especially under settings without LiDAR data. Our method extends prior object-centric neural surface reconstruction techniques to address the unique challenges posed by the unbounded street views that are captured with non-object-centric, long and narrow camera trajectories. We delimit the unbounded space into three parts, close-range, distant-view and sky, with aligned cuboid boundaries, and adapt cuboid/hyper-cuboid hash-grids along with road-surface initialization scheme for finer and disentangled representation. To further address the geometric errors arising from textureless regions and insufficient viewing angles, we adopt geometric priors that are estimated using general purpose monocular models. Coupled with our implementation of efficient and fine-grained multi-stage ray marching strategy, we achieve state of the art reconstruction quality in both geometry and appearance within only one to two hours of training time with a single RTX3090 GPU for each street view sequence. Furthermore, we demonstrate that the reconstructed implicit surfaces have rich potential for various downstream tasks, including ray tracing and LiDAR simulation.

Foveated Neural Radiance Fields for Real-Time and Egocentric Virtual Reality

Traditional high-quality 3D graphics requires large volumes of fine-detailed scene data for rendering. This demand compromises computational efficiency and local storage resources. Specifically, it becomes more concerning for future wearable and portable virtual and augmented reality (VR/AR) displays. Recent approaches to combat this problem include remote rendering/streaming and neural representations of 3D assets. These approaches have redefined the traditional local storage-rendering pipeline by distributed computing or compression of large data. However, these methods typically suffer from high latency or low quality for practical visualization of large immersive virtual scenes, notably with extra high resolution and refresh rate requirements for VR applications such as gaming and design. Tailored for the future portable, low-storage, and energy-efficient VR platforms, we present the first gaze-contingent 3D neural representation and view synthesis method. We incorporate the human psychophysics of visual- and stereo-acuity into an egocentric neural representation of 3D scenery. Furthermore, we jointly optimize the latency/performance and visual quality, while mutually bridging human perception and neural scene synthesis, to achieve perceptually high-quality immersive interaction. Both objective analysis and subjective study demonstrate the effectiveness of our approach in significantly reducing local storage volume and synthesis latency (up to 99% reduction in both data size and computational time), while simultaneously presenting high-fidelity rendering, with perceptual quality identical to that of fully locally stored and rendered high-quality imagery.