The booming of 3D recognition in the 2020s began with the introduction of
point cloud transformers. They quickly overwhelmed sparse CNNs and became
state-of-the-art models, especially in 3D semantic segmentation. However,
sparse CNNs are still valuable networks, due to their efficiency treasure, and
ease of application. In this work, we reexamine the design distinctions and
test the limits of what a sparse CNN can achieve. We discover that the key
credit to the performance difference is adaptivity. Specifically, we propose
two key components, i.e., adaptive receptive fields (spatially) and adaptive
relation, to bridge the gap. This exploration led to the creation of
Omni-Adaptive 3D CNNs (OA-CNNs), a family of networks that integrates a
lightweight module to greatly enhance the adaptivity of sparse CNNs at minimal
computational cost. Without any self-attention modules, OA-CNNs favorably
surpass point transformers in terms of accuracy in both indoor and outdoor
scenes, with much less latency and memory cost. Notably, it achieves 76.1%,
78.9%, and 70.6% mIoU on ScanNet v2, nuScenes, and SemanticKITTI validation
benchmarks respectively, while maintaining at most 5x better speed than
transformer counterparts. This revelation highlights the potential of pure
sparse CNNs to outperform transformer-related networks.

通过提出自适应感受野和自适应关系两个核心组件，我们发现在保持计算成本最小的前提下，纯稀疏卷积神经网络在室内外场景中的准确性方面优于点云变换网络，且速度至少有 5 倍之多的提升。这一发现突显了纯稀疏卷积神经网络超越变换网络的潜力。

OA-CNNs: 三维语义分割的全能自适应稀疏卷积神经网络

OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation

Deep convolutional neural networks (CNNs) are powerful tools for a wide range
of vision tasks, but the enormous amount of memory and compute resources
required by CNNs pose a challenge in deploying them on constrained devices.
Existing compression techniques, while excelling at reducing model sizes,
struggle to be computationally friendly. In this paper, we attend to the
statistical properties of sparse CNNs and present focused quantization, a novel
quantization strategy based on power-of-two values, which exploits the weight
distributions after fine-grained pruning. The proposed method dynamically
discovers the most effective numerical representation for weights in layers
with varying sparsities, significantly reducing model sizes. Multiplications in
quantized CNNs are replaced with much cheaper bit-shift operations for
efficient inference. Coupled with lossless encoding, we built a compression
pipeline that provides CNNs with high compression ratios (CR), low computation
cost and minimal loss in accuracy. In ResNet-50, we achieved a 18.08x CR with
only 0.24% loss in top-5 accuracy, outperforming existing compression methods.
We fully compressed a ResNet-18 and found that it is not only higher in CR and
top-5 accuracy, but also more hardware efficient as it requires fewer logic
gates to implement when compared to other state-of-the-art quantization methods
assuming the same throughput.

本文提出一种基于二次幂值的、专注于稀疏 CNN 的量化策略，并结合无损编码构建一种高压缩比、计算成本低、准确率几乎无损的压缩管线，在 ResNet-50 网络上实现 18.08x CR 的压缩效果，同时也证明更硬件有效。