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LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis (2404.02742v1)

Published 3 Apr 2024 in cs.CV

Abstract: Although neural radiance fields (NeRFs) have achieved triumphs in image novel view synthesis (NVS), LiDAR NVS remains largely unexplored. Previous LiDAR NVS methods employ a simple shift from image NVS methods while ignoring the dynamic nature and the large-scale reconstruction problem of LiDAR point clouds. In light of this, we propose LiDAR4D, a differentiable LiDAR-only framework for novel space-time LiDAR view synthesis. In consideration of the sparsity and large-scale characteristics, we design a 4D hybrid representation combined with multi-planar and grid features to achieve effective reconstruction in a coarse-to-fine manner. Furthermore, we introduce geometric constraints derived from point clouds to improve temporal consistency. For the realistic synthesis of LiDAR point clouds, we incorporate the global optimization of ray-drop probability to preserve cross-region patterns. Extensive experiments on KITTI-360 and NuScenes datasets demonstrate the superiority of our method in accomplishing geometry-aware and time-consistent dynamic reconstruction. Codes are available at https://github.com/ispc-lab/LiDAR4D.

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Citations (5)
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Summary

  • The paper introduces a 4D hybrid representation that combines multi-planar and grid features to efficiently synthesize dynamic LiDAR scenes.
  • It employs geometric constraints and temporal consistency to enhance reconstruction accuracy for large-scale, dynamic environments.
  • The method reduces Chamfer Distance error by 24.3% on KITTI-360 and outperforms existing neural reconstruction approaches.

Overview of "LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis"

The paper "LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis" addresses a critical gap in the field of LiDAR-based novel view synthesis (NVS), focusing on dynamic scene reconstruction. While prior research has primarily concentrated on static scenes and drawing parallels with image-based NVS, this work substantially contributes by introducing a framework tailored for the unique characteristics and challenges of LiDAR data.

Key Contributions and Methodology

The authors introduce LiDAR4D, a differentiable framework that utilizes novel space-time neural fields to achieve realistic LiDAR point cloud synthesis. The framework proposes several innovations:

  1. 4D Hybrid Representation: The authors develop a coarse-to-fine approach using a 4D hybrid representation that combines multi-planar and grid features. This is tailored for handling the large-scale and sparse nature of LiDAR data. The hybrid representation offers an increased resolution and efficient large-scale scene reconstruction, which is pivotal in capturing both static and dynamic elements in autonomous driving scenarios.
  2. Geometric Constraints and Temporal Consistency: To enhance temporal consistency and manage dynamic objects, LiDAR4D incorporates geometric constraints derived from point clouds. This is crucial for maintaining the integrity of dynamic scenes, where alignment and temporal coherence are challenging due to the large motion of objects.
  3. Ray-drop Probability Optimization: The paper tackles the problem of synthesizing realistic LiDAR point clouds by optimizing ray-drop probabilities. This ensures that cross-region patterns are preserved, providing further realism in synthesized outputs.

Experimental Results

The paper validates its claims through extensive experiments using the KITTI-360 and NuScenes datasets. Results indicate that LiDAR4D significantly outperforms existing NeRF-based and explicit reconstruction methods. The authors highlight key performance metrics such as a reduction of 24.3% in Chamfer Distance (CD) error on KITTI-360, affirming the method's superiority. Such improvements underscore LiDAR4D's efficacy in dynamic and large-scale scene reconstructions over previous state-of-the-art approaches.

Implications and Future Directions

LiDAR4D's contributions have substantial implications for applications in AR/VR, robotics, and particularly autonomous driving, where understanding and synthesizing dynamic scenes are pivotal. The introduction of hybrid representations and temporal consistency improvements may pave the way for further exploration in real-time applications and enhanced scene understanding.

Additionally, the paper suggests potential for further refinement and application. Future work could delve into optimizing for even more extensive dynamic scenes or integrating complementary modalities, such as RGB data, to enrich the scene reconstruction further. There is also room for improvement in handling occlusion and long-distance motion challenges, as noted by the authors.

In summary, this work marks a significant advancement in dynamic LiDAR NVS, providing a comprehensive framework that effectively addresses key challenges in the field. Its methodologies and findings serve as a foundation for subsequent research, promoting enhanced realism and accuracy in LiDAR-based reconstruction and synthesis tasks.

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