LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR (2302.14052v1)

Abstract: Scene completion refers to obtaining dense scene representation from an incomplete perception of complex 3D scenes. This helps robots detect multi-scale obstacles and analyse object occlusions in scenarios such as autonomous driving. Recent advances show that implicit representation learning can be leveraged for continuous scene completion and achieved through physical constraints like Eikonal equations. However, former Eikonal completion methods only demonstrate results on watertight meshes at a scale of tens of meshes. None of them are successfully done for non-watertight LiDAR point clouds of open large scenes at a scale of thousands of scenes. In this paper, we propose a novel Eikonal formulation that conditions the implicit representation on localized shape priors which function as dense boundary value constraints, and demonstrate it works on SemanticKITTI and SemanticPOSS. It can also be extended to semantic Eikonal scene completion with only small modifications to the network architecture. With extensive quantitative and qualitative results, we demonstrate the benefits and drawbacks of existing Eikonal methods, which naturally leads to the new locally conditioned formulation. Notably, we improve IoU from 31.7% to 51.2% on SemanticKITTI and from 40.5% to 48.7% on SemanticPOSS. We extensively ablate our methods and demonstrate that the proposed formulation is robust to a wide spectrum of implementation hyper-parameters. Codes and models are publicly available at https://github.com/AIR-DISCOVER/LODE.

• The paper introduces LODE, which integrates localized shape priors into the Eikonal formulation for implicit scene completion from sparse LiDAR data.
• It employs a hybrid neural network that combines sparse convolutions and differentiable trilinear sampling to enforce high-dimensional shape constraints.
• LODE significantly boosts IoU scores on SemanticKITTI and SemanticPOSS, demonstrating enhanced 3D scene reconstruction for autonomous driving.

An Expert Analysis of "LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR"

The paper entitled "LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR" presents a novel approach to implicit scene completion, addressing challenges encountered when using conventional Eikonal methods on sparse LiDAR data. Traditional methodologies have struggled to handle non-watertight and sparse datasets typical of large-scale outdoor environments, which are characteristic of LiDAR sensors used in autonomous driving scenarios.

Key Contributions

The research introduces LODE, a locally conditioned Eikonal formulation, which integrates localized shape priors as dense boundary value constraints in the learning of a signed distance function (SDF). The incorporation of shape priors helps alleviate issues arising from sparsity and inaccurate normal estimations. The proposed method demonstrates significant improvement in intersection over union (IoU) scores, from 31.7% to 51.2% on SemanticKITTI and from 40.5% to 48.7% on SemanticPOSS, highlighting its efficacy over previous approaches.

Methodological Advancements

The core innovation lies in the integration of a discriminative model to generate shape embeddings, which serve as conditioned inputs for the generative model that approximates the SDF. This hybrid neural network approach resolves the limitations imposed by previous Eikonal methods in sparse point clouds by enforcing constraints in a high-dimensional embedding space rather than directly in Cartesian space.

Key methodological choices include:

• Sparse Convolutional Networks: Utilization of sparse convolutions to encode localized shape priors from point clouds, effectively learning a shape embedding space to aid completed scene representation.
• Differentiable Trilinear Sampling: This mechanism ensures that gradient flow and practical interpolations between the shape embeddings and SDF are preserved during backpropagation.
• Locally Conditioned Eikonal Formulation: The execution of the Eikonal equation within a conditioned latent space reflects scene-specific geometric priors, which enhance fitting accuracy and robustness to noise.

Implications and Future Directions

Practically, LODE paves the way for more robust 3D scene completion techniques in autonomous driving and robotics, where real-time, high-fidelity environment perception is critical. By effectively managing sparse sensor data, LODE can potentially improve object detection and occlusion analysis without requiring densely annotated datasets, a common limiting factor in deployment.

Theoretically, this research expands the potential of implicit neural representations by showing how learned priors facilitate the completion of complex scenes. Future work may explore extending these methods to incorporate multi-modal data inputs (e.g., integrating RGB images), further expanding potential application domains.

The results of LODE also suggest potential interoperability with downstream tasks such as path planning and obstacle avoidance, where the quality of scene representation directly affects performance. Lastly, improvements in semantic scene completion, illustrated by semantic extensions in the paper, demonstrate flexible applicability across diverse robotic and visual perception tasks.

Conclusion

In summary, "LODE" addresses a significant gap in Eikonal-based implicit scene completion methods by introducing locally conditioned formulations and leveraging advanced neural network architectures tailored to sparse LiDAR data. The extensive empirical evaluation and demonstration of practical benefits suggest a strong foundation for further advancements in 3D perception fields. The public availability of codes and models is poised to facilitate future research and application in this domain.