This paper presents MinkUNeXt, an effective and efficient architecture for
place-recognition from point clouds entirely based on the new 3D MinkNeXt
Block, a residual block composed of 3D sparse convolutions that follows the
philosophy established by recent Transformers but purely using simple 3D
convolutions. Feature extraction is performed at different scales by a U-Net
encoder-decoder network and the feature aggregation of those features into a
single descriptor is carried out by a Generalized Mean Pooling (GeM). The
proposed architecture demonstrates that it is possible to surpass the current
state-of-the-art by only relying on conventional 3D sparse convolutions without
making use of more complex and sophisticated proposals such as Transformers,
Attention-Layers or Deformable Convolutions. A thorough assessment of the
proposal has been carried out using the Oxford RobotCar and the In-house
datasets. As a result, MinkUNeXt proves to outperform other methods in the
state-of-the-art.

MinkUNeXt 是一种基于 3D MinkNeXt Block 的高效有效的基于点云的地点识别架构，该架构通过 3D 稀疏卷积实现特征提取和聚合，并在测试中表现出优于其他方法的结果。

MinkUNeXt：基于稀疏三维卷积的基于点云的大规模地点识别

MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D  Sparse Convolutions

Convolution models with long filters have demonstrated state-of-the-art
reasoning abilities in many long-sequence tasks but lag behind the most
optimized Transformers in wall-clock time. A major bottleneck is the Fast
Fourier Transform (FFT)--which allows long convolutions to run in $O(N logN)$
time in sequence length $N$ but has poor hardware utilization. In this paper,
we study how to optimize the FFT convolution. We find two key bottlenecks: the
FFT does not effectively use specialized matrix multiply units, and it incurs
expensive I/O between layers of the memory hierarchy. In response, we propose
FlashFFTConv. FlashFFTConv uses a matrix decomposition that computes the FFT
using matrix multiply units and enables kernel fusion for long sequences,
reducing I/O. We also present two sparse convolution algorithms--1) partial
convolutions and 2) frequency-sparse convolutions--which can be implemented
simply by skipping blocks in the matrix decomposition, enabling further
opportunities for memory and compute savings. FlashFFTConv speeds up exact FFT
convolutions by up to 7.93$\times$ over PyTorch and achieves up to 4.4$\times$
speedup end-to-end. Given the same compute budget, FlashFFTConv allows
Hyena-GPT-s to achieve 2.3 points better perplexity on the PILE and
M2-BERT-base to achieve 3.3 points higher GLUE score--matching models with
twice the parameter count. FlashFFTConv also achieves 96.1% accuracy on
Path-512, a high-resolution vision task where no model had previously achieved
better than 50%. Furthermore, partial convolutions enable longer-sequence
models--yielding the first DNA model that can process the longest human genes
(2.3M base pairs)--and frequency-sparse convolutions speed up pretrained models
while maintaining or improving model quality.

我们提出了 FlashFFTConv，一个优化 FFT 卷积的方法，使用矩阵分解结构和稀疏卷积算法实现了长序列任务的加速，同时在计算资源限制下，提高了模型的性能和准确度。

FlashFFTConv：基于张量核的长序列高效卷积

FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor  Cores

Recent Transformer-based 3D object detectors learn point cloud features
either from point- or voxel-based representations. However, the former requires
time-consuming sampling while the latter introduces quantization errors. In
this paper, we present a novel Point-Voxel Transformer for single-stage 3D
detection (PVT-SSD) that takes advantage of these two representations.
Specifically, we first use voxel-based sparse convolutions for efficient
feature encoding. Then, we propose a Point-Voxel Transformer (PVT) module that
obtains long-range contexts in a cheap manner from voxels while attaining
accurate positions from points. The key to associating the two different
representations is our introduced input-dependent Query Initialization module,
which could efficiently generate reference points and content queries. Then,
PVT adaptively fuses long-range contextual and local geometric information
around reference points into content queries. Further, to quickly find the
neighboring points of reference points, we design the Virtual Range Image
module, which generalizes the native range image to multi-sensor and
multi-frame. The experiments on several autonomous driving benchmarks verify
the effectiveness and efficiency of the proposed method. Code will be available
at this https URL

本论文提供了一种新型的点块 Transformer 用于单级三维检测 (PVT-SSD), 其使用基于体素的稀疏卷积进行有效的特征编码，并从体素中以便宜的方式获取长距离上下文，同时从点中获取准确的位置并通过引入依赖于输入的查询初始化模块关联这两种不同的表示。进一步地，通过设计 Virtual Range Image 模块，该方法能够快速找到参考点的邻近点。该方法在几个自动驾驶基准测试中得到了验证，表明其有效性和高效性。

PVT-SSD：使用点 - 体素变换的单级三维物体探测器

PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer

In this work we target the problem of estimating accurately localised
correspondences between a pair of images. We adopt the recent Neighbourhood
Consensus Networks that have demonstrated promising performance for difficult
correspondence problems and propose modifications to overcome their main
limitations: large memory consumption, large inference time and poorly
localised correspondences. Our proposed modifications can reduce the memory
footprint and execution time more than $10\times$, with equivalent results.
This is achieved by sparsifying the correlation tensor containing tentative
matches, and its subsequent processing with a 4D CNN using submanifold sparse
convolutions. Localisation accuracy is significantly improved by processing the
input images in higher resolution, which is possible due to the reduced memory
footprint, and by a novel two-stage correspondence relocalisation module. The
proposed Sparse-NCNet method obtains state-of-the-art results on the HPatches
Sequences and InLoc visual localisation benchmarks, and competitive results in
the Aachen Day-Night benchmark.

通过采用邻域共识网络并对其进行修改，运用 4DCNN 和稀疏卷积，提高了图像间的精确本地化对应关系，同时在 HPatches Sequences 以及 InLoc 视觉本地化基准中达到最先进的结果。