arxiv: 2604.17720 · v1 · submitted 2026-04-20 · 💻 cs.LG · cs.CV

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FlashFPS: Efficient Farthest Point Sampling for Large-Scale Point Clouds via Pruning and Caching

Yuzhe Fu , Hancheng Ye , Cong Guo , Junyao Zhang , Qinsi Wang , Yueqian Lin , Changchun Zhou , Hai (Helen) Li

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Yiran Chen

Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:53 UTC · model grok-4.3

classification 💻 cs.LG cs.CV

keywords farthest point samplingpoint cloud processingpoint-based neural networkspruningcachinginference accelerationGPU optimization

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The pith

FlashFPS accelerates farthest point sampling in point-based neural networks by pruning redundant computations and caching inter-layer results.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to prove that the standard farthest point sampling step inside point-based neural networks contains three clear redundancies that can be removed without hurting sampling quality. These are full-cloud work that is not needed, late iterations that add little value, and outputs between layers that can be predicted and reused. A reader would care because this operation is a repeated bottleneck when processing large point clouds, slowing down inference on both GPUs and specialized accelerators. If the reductions hold, networks can run substantially faster while remaining plug-and-play with existing code libraries.

Core claim

The authors show that farthest point sampling across multiple layers of a point-based network repeats three kinds of unnecessary work: computing distances over the entire cloud when only a subset matters, continuing iterations after most useful points have been chosen, and recalculating outputs that are already known from earlier layers. FPS-Prune removes the first two by candidate pruning and iteration pruning. FPS-Cache stores and reuses the third. When these are added to current CUDA implementations and hardware accelerators, the same sampling quality is retained at far lower cost.

What carries the argument

FPS-Prune and FPS-Cache, a pair of techniques that prune candidate points and late iterations while storing and reusing predictable outputs between network layers.

If this is right

Candidate pruning can safely shrink the set of points considered in each FPS round.
Iteration pruning can stop the sampling loop early once additional points add little new information.
Caching inter-layer outputs can eliminate repeated distance calculations across stacked network layers.
The combined changes integrate directly into existing CUDA kernels and accelerator designs to deliver the reported speedups.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same redundancy patterns may exist in other iterative sampling or clustering routines used in 3D vision.
Hardware designers could expose simple cache hints for layer outputs to make the reuse step even cheaper.
Varying the aggressiveness of candidate and iteration pruning could produce tunable speed-accuracy curves for different applications.

Load-bearing premise

The three redundancies appear in the workloads of interest and can be removed by pruning or caching without more than negligible harm to the quality of the final sampled points.

What would settle it

Apply the pruning and caching steps to a point-based network on a standard large-scale point cloud benchmark and measure either no wall-clock speedup or a clear drop in downstream task accuracy.

Figures

Figures reproduced from arXiv: 2604.17720 by Changchun Zhou, Cong Guo, Hai (Helen) Li, Hancheng Ye, Junyao Zhang, Qinsi Wang, Yiran Chen, Yueqian Lin, Yuzhe Fu.

**Figure 2.** Figure 2: The inefficiency stems from the iterative greedy nature of [PITH_FULL_IMAGE:figures/full_fig_p001_2.png] view at source ↗

**Figure 2.** Figure 2: The latency breakdown for PointNeXt-L and [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Illustration of iteratively greedy selection in FPS. [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗

**Figure 4.** Figure 4: Point distributions after FPS for (a) the original and [PITH_FULL_IMAGE:figures/full_fig_p002_4.png] view at source ↗

**Figure 6.** Figure 6: FlashFPS: a plug-and-play acceleration framework that integrates FPS-Prune (in-layer pruning) and FPS-Cache [PITH_FULL_IMAGE:figures/full_fig_p003_6.png] view at source ↗

**Figure 7.** Figure 7: shows the detailed GPU speedup of FlashFPS on PointNeXt-L and PointVector-L using the S3DIS and ScanNet datasets under four representative point numbers. We compare against the CUDAoptimized FPS baseline (FPS-CUDA, from OpenPoints [20]) and QuickFPS [12], which is integrated into the same framework for a fair end-to-end comparison. QuickFPS improves memory access and skips partial redundant computations, … view at source ↗

**Figure 8.** Figure 8: Speedup of FlashFPS on different hardware plat [PITH_FULL_IMAGE:figures/full_fig_p005_8.png] view at source ↗

**Figure 9.** Figure 9: Memory footprint comparison of network infer [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗

read the original abstract

Point-based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sampling (FPS), often introduces significant inference latency, especially for large-scale processing. Despite existing CUDA- and hardware-level optimizations, FPS remains a major bottleneck due to exhaustive computations across multiple network layers in PNNs, which hinders scalability. Through systematic analysis, we identify three substantial redundancies in FPS, including unnecessary full-cloud computations, redundant late-stage iterations, and predictable inter-layer outputs that make later FPS computations avoidable. To address these, we propose \textbf{\textit{FlashFPS}}, a hardware-agnostic, plug-and-play framework for FPS acceleration, composed of \textit{FPS-Prune} and \textit{FPS-Cache}. \textit{FPS-Prune} introduces candidate pruning and iteration pruning to reduce redundant computations in FPS while preserving sampling quality, and \textit{FPS-Cache} eliminates layer-wise redundancy via cache-and-reuse. Integrated into existing CUDA libraries and state-of-the-art PNN accelerators, \textit{FlashFPS} achieves 5.16$\times$ speedup over the standard CUDA baseline on GPU and 2.69$\times$ on PNN accelerators, with negligible accuracy loss, enabling efficient and scalable PNN inference. Codes are released at https://github.com/Yuzhe-Fu/FlashFPS.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

FlashFPS delivers measurable FPS speedups for point cloud nets through pruning and caching, with code out to check the claims.

read the letter

FlashFPS gives a practical speedup for farthest point sampling in large point clouds by pruning redundant computations and caching across layers in point-based networks. The work identifies three redundancies—full-cloud scans, late-stage iterations, and inter-layer repeats—and removes them with FPS-Prune and FPS-Cache. This combination is new compared to existing CUDA optimizations. They plug it into standard libraries and PNN accelerators. The paper does well by showing 5.16 times faster on GPU and 2.69 times on accelerators with almost no accuracy loss on benchmarks. Releasing the code is helpful for checking the implementation and results. The analysis of redundancies is clear and the methods follow from it without overcomplicating things. Integration into existing systems shows it's plug-and-play as claimed. The results are measured against the standard baseline, which is the right comparison. Since the code is out, anyone can run the experiments to confirm the speedups and see if the sampled points match closely enough for downstream tasks. Soft spots are limited. The pruning rules may require tuning for new datasets or models, and more detail on how thresholds were set would help. The accelerator improvement is smaller, but that fits with hardware differences. The accuracy preservation is tested directly, so the main claim holds. One potential issue is whether these redundancies exist in all PNN architectures or only the ones tested, but the paper focuses on common ones. This is for engineers and researchers working on efficient 3D perception systems. It has enough solid empirical grounding to go to peer review.

Referee Report

0 major / 3 minor

Summary. The paper claims that Farthest Point Sampling (FPS) in Point-based Neural Networks (PNNs) contains three exploitable redundancies (full-cloud computations, late-stage iterations, and predictable inter-layer outputs). It introduces the hardware-agnostic FlashFPS framework consisting of FPS-Prune (candidate pruning plus iteration pruning) and FPS-Cache (inter-layer reuse) that can be plugged into existing CUDA libraries and PNN accelerators. End-to-end experiments report 5.16× GPU speedup and 2.69× accelerator speedup versus standard baselines while preserving downstream PNN accuracy, with code released at https://github.com/Yuzhe-Fu/FlashFPS.

Significance. If the reported speedups and accuracy preservation hold under the stated pruning rules, the work would meaningfully reduce a well-known inference bottleneck for large-scale point-cloud models, improving practicality of PNNs in real-time or resource-constrained settings. The explicit pruning logic, cache mechanism, and public code release constitute verifiable strengths that support adoption and further optimization.

minor comments (3)

[§4 / §5] The manuscript would benefit from a short table or paragraph in §4 or §5 that lists the exact pruning thresholds (e.g., candidate ratio, iteration cutoff) used for each benchmark, as these values are central to reproducing the claimed speedups.
[§3.2] Figure 3 (or equivalent) showing cache hit rates across layers would clarify the contribution of FPS-Cache; currently the text describes the mechanism but does not quantify its isolated impact.
[§5.2] A brief comparison of sampled-point distributions (e.g., Chamfer distance or coverage metrics between original FPS and FlashFPS) in addition to downstream accuracy would strengthen the claim that sampling quality is preserved.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive review, recognition of the practical impact of FlashFPS on reducing FPS bottlenecks in point-based networks, and recommendation to accept the manuscript.

Circularity Check

0 steps flagged

No significant circularity; empirical optimization validated by external measurements

full rationale

The paper is an engineering contribution that identifies three redundancies in FPS via systematic analysis and introduces FPS-Prune and FPS-Cache as plug-and-play optimizations. All load-bearing claims (5.16× GPU speedup, 2.69× accelerator speedup, negligible accuracy loss) are supported by direct end-to-end timing and accuracy measurements on real PNN workloads against independent CUDA and accelerator baselines, plus released code. No equations, fitted parameters, or predictions appear; no self-citations are invoked as uniqueness theorems or to justify core premises. The derivation chain is therefore self-contained against external benchmarks rather than reducing to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

No free parameters are introduced; the method relies on standard FPS definition and empirical pruning heuristics whose thresholds are not detailed in the abstract. No new entities postulated.

axioms (1)

standard math FPS definition as iterative selection of the point farthest from already selected points
Invoked in the description of redundancies and pruning rules.

pith-pipeline@v0.9.0 · 5591 in / 1111 out tokens · 26778 ms · 2026-05-10T04:53:16.557588+00:00 · methodology

discussion (0)

Reference graph

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