LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation (2412.15199v1)

Abstract: This paper targets the challenge of real-time LiDAR re-simulation in dynamic driving scenarios. Recent approaches utilize neural radiance fields combined with the physical modeling of LiDAR sensors to achieve high-fidelity re-simulation results. Unfortunately, these methods face limitations due to high computational demands in large-scale scenes and cannot perform real-time LiDAR rendering. To overcome these constraints, we propose LiDAR-RT, a novel framework that supports real-time, physically accurate LiDAR re-simulation for driving scenes. Our primary contribution is the development of an efficient and effective rendering pipeline, which integrates Gaussian primitives and hardware-accelerated ray tracing technology. Specifically, we model the physical properties of LiDAR sensors using Gaussian primitives with learnable parameters and incorporate scene graphs to handle scene dynamics. Building upon this scene representation, our framework first constructs a bounding volume hierarchy (BVH), then casts rays for each pixel and generates novel LiDAR views through a differentiable rendering algorithm. Importantly, our framework supports realistic rendering with flexible scene editing operations and various sensor configurations. Extensive experiments across multiple public benchmarks demonstrate that our method outperforms state-of-the-art methods in terms of rendering quality and efficiency. Our project page is at https://zju3dv.github.io/lidar-rt.

• The paper introduces LiDAR-RT, a novel framework that uses Gaussian primitives with learnable parameters and hardware-accelerated ray tracing to achieve efficient, high-fidelity LiDAR re-simulation in dynamic scenes.
• LiDAR-RT significantly outperforms state-of-the-art methods, achieving 30 FPS rendering speed and 2-hour training times, compared to 0.2 FPS and up to 15 hours for competing approaches.
• This real-time re-simulation capability has significant implications for digital twins, autonomous driving systems, and virtual reality, enabling more responsive and realistic environments for testing and deployment.

LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation

The paper introduces a novel framework titled LiDAR-RT, aimed at addressing the computational challenges associated with real-time LiDAR re-simulation in dynamic driving scenarios. The authors argue that while existing methods that utilize neural radiance fields deliver high-fidelity simulations, they suffer from excessive computational demands, impairing their real-time applicability. LiDAR-RT proposes an efficient and effective pipeline integrating Gaussian primitives with hardware-accelerated ray tracing to overcome these limitations.

Key Contributions and Methodology

The central contribution of the paper is the development of a new rendering pipeline. This pipeline models the physical properties of LiDAR sensors using Gaussian primitives with learnable parameters, combined with scene graphs to manage dynamic scenes' complexities. The framework constructs a bounding volume hierarchy (BVH) and executes ray tracing for each pixel to create novel LiDAR views via a differentiable rendering algorithm.

The innovative use of Gaussian primitives augments the standard formulation with learnable parameters to model intrinsic sensor properties like reflection intensity and ray-drop probability. This is managed with spatially-conditioned scene graphs that allow flexible modeling under varying environmental conditions.

When simulating the LiDAR sensor's physical formation, the authors utilize a differentiable Gaussian-based ray tracer. This tracer constructs proxy geometries for the Gaussian primitives, integrates them into a BVH, and calculates LiDAR radiance by ray tracing through the hierarchy. This method enhances both realism and accuracy compared to traditional rasterization techniques, achieving high fidelity and real-time rendering capabilities.

Experimental Validation

The paper demonstrates that LiDAR-RT outperforms state-of-the-art (SOTA) methods across various public benchmarks. In experiments conducted on the Waymo Open and KITTI-360 datasets, LiDAR-RT not only delivered superior rendering quality but also significantly improved computational efficiency. Notably, the framework achieved a rendering speed of 30 FPS with substantially reduced training durations (2 hours) compared to competing methods, which required up to 15 hours and managed only 0.2 FPS.

Implications and Future Directions

The research has significant implications for applications requiring robust and flexible simulations of LiDAR data, such as digital twin environments, autonomous driving systems, and virtual reality interfaces. The ability to perform accurate, real-time re-simulations of dynamic urban environments could enhance these application areas by providing more responsive and realistic environments for testing and deployment.

Looking towards future research, challenges remain in fully integrating non-rigid object modeling and managing large-scale scenes with long sequences, where the number of Gaussian primitives may increase substantially. Extending the framework to address these limitations would further enhance its applicability and utility across various demanding computational scenarios in AI and beyond.

The notion of Gaussian-based modeling combined with ray tracing might also inspire future developments in the representation of other sensor models or even the integration of multi-modal sensor data, thereby expanding the horizon for real-time, high-fidelity 3D re-simulations.