LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs (2206.10555v2)

Abstract: Recent advance in 2D CNNs has revealed that large kernels are important. However, when directly applying large convolutional kernels in 3D CNNs, severe difficulties are met, where those successful module designs in 2D become surprisingly ineffective on 3D networks, including the popular depth-wise convolution. To address this vital challenge, we instead propose the spatial-wise partition convolution and its large-kernel module. As a result, it avoids the optimization and efficiency issues of naive 3D large kernels. Our large-kernel 3D CNN network, LargeKernel3D, yields notable improvement in 3D tasks of semantic segmentation and object detection. It achieves 73.9% mIoU on the ScanNetv2 semantic segmentation and 72.8% NDS nuScenes object detection benchmarks, ranking 1st on the nuScenes LIDAR leaderboard. The performance further boosts to 74.2% NDS with a simple multi-modal fusion. In addition, LargeKernel3D can be scaled to 17x17x17 kernel size on Waymo 3D object detection. For the first time, we show that large kernels are feasible and essential for 3D visual tasks.

• The paper introduces spatial-wise partition convolution to efficiently scale large kernels in 3D sparse CNNs without excessive parameter increases.
• It demonstrates significant performance gains on benchmarks like ScanNetv2 and nuScenes, including a top nuScenes LIDAR score of 74.2% NDS using multi-modal fusion.
• The research underscores the challenges and solutions for adapting successful 2D kernel techniques to 3D CNNs, paving the way for improved 3D perception in autonomous systems.

LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs

In the pursuit of enhancing 3D visual tasks such as semantic segmentation and object detection, the paper "LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs" advances a novel approach to leverage the potential of large kernels in 3D sparse convolutional networks (CNNs). While large kernels have demonstrated utility in 2D CNNs by improving receptive field and model capacity, these benefits have not directly translated to 3D CNNs due to several critical challenges. This paper introduces spatial-wise partition convolution as a pivotal innovation to address efficiency and optimization obstacles associated with naive application of large kernels in 3D sparse CNNs.

Key Contributions and Results

1. Spatial-wise Partition Convolution: The proposed method divides the large spatial kernels into small spatial segments and shares weights among spatially adjacent locations. This design efficiently manages computational complexity without increasing model parameters excessively, accommodating large kernels while maintaining the integrity of sparse data.
2. Empirical Validation: The LargeKernel3D network achieves significant performance improvements on established benchmarks such as ScanNetv2 and nuScenes. Notably, it ranks first on the nuScenes LIDAR leaderboard with a score of 72.8% NDS, achieving further improvement to 74.2% NDS when utilizing a simple multi-modal fusion approach.
3. Scalability of Large Kernels: Demonstrating scalability, LargeKernel3D successfully employs kernel sizes up to 17x17x17 on the Waymo 3D object detection benchmark, underscoring the feasibility of large kernels for extensive 3D tasks.
4. Performance and Efficiency Comparison: Extensive ablation studies highlight that many popular techniques beneficial in 2D CNNs, such as depth-wise convolution and GELU, do not necessarily translate to 3D networks. In contrast, spatial-wise partition convolution outperforms existing paradigms by effectively managing receptive fields and improving optimization.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the adoption of LargeKernel3D in 3D tasks can potentiate further advancements in areas like autonomous driving and robotics, where 3D perception is crucial. Theoretically, the approach indicates a new direction for enhancing the scale of kernels in high-dimensional convolutional architectures without succumbing to the inefficiency and parameter bloat common in naive large-kernel applications.

Speculation on future developments involves the exploration of adaptive techniques for dynamic spatial partitioning and the integration of learning-based optimization strategies. Additionally, investigating the synergy between large kernel designs and transformer-based architectures, particularly in multi-modal environments, could yield further performance gains.

In conclusion, LargeKernel3D makes a compelling case for the necessity and practicality of large kernels in 3D CNNs through its innovative spatial-wise partitioning approach. This paper lays a foundation for future research to explore the nuanced interplay between kernel size, computational efficiency, and optimized representation learning in 3D environments.