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Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation (2404.11958v1)

Published 18 Apr 2024 in cs.CV and cs.RO

Abstract: Semantic scene completion, also known as semantic occupancy prediction, can provide dense geometric and semantic information for autonomous vehicles, which attracts the increasing attention of both academia and industry. Unfortunately, existing methods usually formulate this task as a voxel-wise classification problem and treat each voxel equally in 3D space during training. As the hard voxels have not been paid enough attention, the performance in some challenging regions is limited. The 3D dense space typically contains a large number of empty voxels, which are easy to learn but require amounts of computation due to handling all the voxels uniformly for the existing models. Furthermore, the voxels in the boundary region are more challenging to differentiate than those in the interior. In this paper, we propose HASSC approach to train the semantic scene completion model with hardness-aware design. The global hardness from the network optimization process is defined for dynamical hard voxel selection. Then, the local hardness with geometric anisotropy is adopted for voxel-wise refinement. Besides, self-distillation strategy is introduced to make training process stable and consistent. Extensive experiments show that our HASSC scheme can effectively promote the accuracy of the baseline model without incurring the extra inference cost. Source code is available at: https://github.com/songw-zju/HASSC.

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Citations (7)
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Summary

  • The paper presents a hardness-aware framework that dynamically prioritizes challenging voxels to boost semantic scene completion accuracy.
  • It introduces global and local hardness measures by assessing model uncertainty and geometric anisotropy to refine voxel predictions.
  • Self-distillation is employed to transfer robust knowledge from a teacher model, yielding significant mIoU improvements on the SemanticKITTI benchmark.

Hardness-Aware Semantic Scene Completion for Autonomous Vehicles

In the sphere of computer vision and semantic scene understanding, the paper "Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation" addresses the task of semantic scene completion (SSC) — a crucial component in autonomous vehicle navigation. The authors present an innovative approach, named HASSC (Hardness-Aware Semantic Scene Completion), that redefines conventional SSC models by acknowledging the varying degrees of complexity among voxels in 3D space.

Core Contributions and Methodology

The paper introduces a hardness-aware design that challenges the baseline assumption that all voxels are of equal significance during training. The HASSC approach considers both global and local hardness factors:

  1. Global Hardness: This factor captures the uncertainty in predicting each voxel, dynamically guiding the selection of challenging voxels during training. The measure of hardness is derived from the model's output probabilities, with greater attention being paid to voxels where the class distinction is less clear.
  2. Local Hardness: This concept captures the semantic differences among neighboring voxels using the local geometric anisotropy. It essentially provides a refined focus on those voxels situated at object boundaries, where prediction difficulty is naturally higher.
  3. Self-Distillation Strategy: The authors implement a self-distillation mechanism that enhances model consistency and reliability by distilling knowledge from a temporarily frozen version of the model (teacher model) to the real-time updating model (student model).

The integration of these elements allows the SSC models to prioritize harder voxels, thus improving prediction accuracy without additional inference latency.

Recapitulation of Results

The authors validate their approach using the SemanticKITTI dataset, a standard benchmark for semantic scene understanding in outdoor environments. The model achieves notable improvements over baseline methods. For instance, HASSC-VoxFormer-T, which integrates the proposed hardness-aware strategy into the VoxFormer architecture, exhibits a substantial increase in mean IoU (mIoU) and IoU metrics. Notably, the improvements are more pronounced in complex scenes where hard voxels are prevalent.

Implications and Future Directions

The research signifies a step forward in SSC by improving the model's capacity to handle occluded or boundary voxels — often the most challenging aspects of scene comprehension in dynamic environments like autonomous driving. The presented results hint at the potential for integrating similar hardness-aware strategies into other dense 3D representation challenges, such as those faced in mixed-field environments involving LiDAR and camera data fusion.

Looking forward, refinements could explore the dynamic adaptation of hardness strategies to further tailor the focus to real-time environmental changes, thus enhancing practical applicability. Moreover, advancements in neural radiance and implicit fields could be leveraged to address structural learning in this context.

In conclusion, this paper contributes a methodological enhancement in interpreting and processing 3D environments, which could impact not just autonomous navigation tasks but also broader applications in robotics and virtual reality.

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