Efficient LiDAR Reflectance Compression via Scanning Serialization

Dandan Ding; Jiahao Zhu; Kang You; Zhan Ma

REVIEW 4 major objections 4 minor 1 cited by

Efficient LiDAR Reflectance Compression via Scanning Serialization

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read SerLiC losslessly compresses LiDAR reflectance to less than half its original size, beating prior learned codecs by up to 22% at 2% of their parameters.

desk verdict Genuinely new serialization idea with solid ablations on the merits, but the abstract's headline gains are computed from the light model while the parameter claim belongs to the standard model — the central numbers must be re-tabled before they can be trusted. read the letter →

arxiv 2505.09433 v3 pith:SDTIRY7V submitted 2025-05-14 cs.CV eess.IV

Jiahao Zhu , Kang You , Dandan Ding , Zhan Ma This is my paper · ORCID

classification cs.CVeess.IV

keywords LiDARreflectancecompressionscan-orderserializationautoregressiveentropycodingstatespacemodelMambalosslesspointcloudsensor-awarecontext

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that lossless compression of LiDAR reflectance can be built on a device-centric inductive bias: reorder the point cloud into 1D sequences that follow the sensor's own scan pattern, then predict each point's reflectance from its scan index, its radial distance, and the reflectance of the immediately preceding point. It proposes SerLiC, which implements this with a Mamba-based autoregressive entropy model and arithmetic coding, and reports over 2× volume reduction against the raw reflectance, up to 22% fewer compressed bits than the previous best learned codec, and a 111K-parameter variant running above 10 frames per second. A sympathetic reader would care because reflectance is essential for downstream tasks such as pedestrian detection, yet prior compression methods treat LiDAR points as an unordered 3D cloud and pay for large 3D convolutions. The paper's core message is that the scan order itself is the correlation structure worth exploiting.

What carries the argument

The load-bearing mechanism is scan-order serialization plus a physics-informed token. Each Cartesian point is mapped to spherical coordinates, binned into a laser index $v_i$ and an azimuth index $u_i$ by Eq. (4), grouped by laser, and sorted by azimuth to form $L$ one-dimensional sequences; the previous point in such a sequence is the physically adjacent reflection of the same rotating beam. Each point is then tokenized as an embedding of $(v_i,u_i)$, a normalized radial distance $\rho_i$, and the already-decoded reflectance of the preceding point, and the token sequence is passed through Mamba blocks whose output feeds a softmax classifier that produces the probability mass function for the current reflectance. A dual parallelization scheme processes sequences independently (justified by an ablation showing inter-sequence context adds no bitrate) and slices each sequence into windows, keeping the autoregressive dependency local while allowing parallel coding. The same machinery is what makes the paper's efficiency claims possible: linear-time sequence modeling replaces the cubic-time masked attention decoding of a windowed transformer.

What would settle it

Take a fixed LiDAR frame and rerun SerLiC on the same coordinates with the point order randomly shuffled before serialization; if the bit rate barely changes, the scan-order prior is not what carries the gains, and if it degrades sharply, the ordering is essential. A second check is to vary the assumed angular resolutions $L$ and $W$, for example by $\pm10\%$ or with a wrong sensor model, and measure bits per point: the paper's physical-grounding argument predicts that mis-specified geometry should measurably erode the 17–23% advantage over the previous learned codec.

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Extended reading notes

Core claim

On its own terms, the paper establishes that LiDAR reflectance has a sequential structure that standard 3D point cloud codecs ignore: points captured by the same rotating laser in consecutive azimuth steps are strongly correlated, including correlations that are not captured by spatial proximity. SerLiC makes this structure explicit by computing a laser index $v_i$ and an azimuth index $u_i$ from each point's spherical coordinates, grouping points by laser, sorting by azimuth, and then coding each point autoregressively with previous reflectance as part of the context. The reported result is that this simple reordering turns lossless reflectance coding into a sequence-prediction problem that a small Mamba network solves well: on KITTI the method reaches 3.90 bits per point versus 4.88 for G-PCC RAHT and 4.39 for Unicorn, and on Ford 4.29 versus 5.16 and 4.97, with roughly 50x fewer parameters than Unicorn and about 10% of its GPU memory. The paper also demonstrates, through removal experiments on three detectors, that reflectance is load-bearing for object detection, which is why compressing it matters.

Load-bearing premise

The load-bearing premise is that sorting points by sensor scan index and then reading them in that order exposes the dominant correlations in reflectance, so that the immediately preceding point's reflectance is the right primary context; if a LiDAR's angular resolutions $L$ and $W$ are unknown, or the point cloud has been preprocessed so that the coordinates no longer preserve the raw scan order, this serialization loses its physical grounding and the claimed gains have no reason to hold.

Editorial extensions

If this is right

Lossless reflectance storage can be cut to roughly half, with reported averages of 3.90 bits per point on KITTI and 4.29 on Ford compared with 4.88 and 5.16 for G-PCC RAHT.
The method transfers to non-rotational LiDAR: on the InnovizQC sequences it reports about 39.7% average bitrate reduction relative to G-PCC RAHT.
Coding sequences independently loses nothing: adding inter-sequence context gave no bitrate gain, so laser sequences can be compressed in parallel without a rate penalty.
Mamba matches masked attention in compression quality while cutting decoding latency dramatically, for example 0.23 seconds versus 5.12 seconds per KITTI frame at window size 128.
A 111K-parameter variant runs above 10 frames per second without frame pipelining and above 30 with it, making real-time use plausible for autonomous-driving-style workloads.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

As an extension of the paper's ablation, prior reflectance alone already delivers a 19.67% gain over G-PCC RAHT on KITTI, which suggests most of the benefit comes from the ordering itself; a cheap testable extension is to replace Mamba with a much smaller 1D predictor or a conditional histogram on the serialized sequences and measure how much of the gain survives.
If scan-order serialization generalizes, similar gains should appear for other sensor-native attributes such as multiple LiDAR intensity channels, or for range-view image compression that uses the same beam-azimuth grid, because the underlying correlation is about the sensor geometry rather than the specific reflectance attribute.
A practical engineering inference is that real deployments would need per-sensor calibration of $L$ and $W$, and possibly learned estimation of those parameters from unlabeled sweeps, since the paper assumes the angular resolutions are known but does not study robustness to miscalibration.
Because the lossless baseline is now cheap and fast, a lossy extension becomes feasible that allocates bits according to downstream detection relevance rather than raw reflectance fidelity; the paper itself flags lossy coding as future work.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague —

The short version: the idea is genuinely new, the ablations are mostly solid, and the headline arithmetic is a mess. SerLiC serializes LiDAR points by scan order (laser index, azimuth index, radial distance) and codes reflectance with a Mamba autoregressive entropy model. That is the right inductive bias for LiDAR reflectance, and no prior neural attribute codec (Unicorn, CNeT/MNeT, PoLoPCAC) does this; L3C2 needs exact sensor parameters and is not neural. The ablations earn their keep: context components each contribute, window-size and depth/width sweeps are sensible, the attention-vs-Mamba comparison is fair, and the supplementary shows inter-sequence modeling buys nothing, which justifies the sequence-level parallelism.

But the headline claims do not survive contact with the paper's own tables. The gain columns in Table 1 are computed against SerLiC-Light, not the standard model. I re-did the arithmetic: on KITTI, the reported 25.41% vs RAHT equals (4.88−3.64)/4.88, where 3.64 is the light model; the standard model gives (4.88−3.90)/4.88 = 20.08%. On Ford, the 22.74% vs Unicorn equals (4.97−3.84)/4.97, again light; standard gives 13.68%. The "over 2× volume reduction" claim is dataset-dependent: 7/3.90 = 1.79× on KITTI and 8/4.29 = 1.86× on Ford at standard settings; only nuScenes (8/2.78 = 2.88×) clears 2×. So the abstract pairs the standard model's 2% parameter count with the light model's bitrate gains.

Worse, the tables contradict each other. Tables 4–6 report the default full model at 3.64 bpp on KITTI with −25.41%; Table 1 credits those exact numbers to the light model and gives the standard model 3.90. Same dataset, same configuration, two different bpp values. Either the ablations used the light config or a table is mislabeled; as printed, the central result is not verifiable from the paper alone. The light model also appears to beat the standard model on KITTI and most nuScenes sequences, which nothing in the ablations explains.

Smaller gaps: no KITTI train/test split is stated, no code, and the serialization premise gets no sensitivity analysis — KITTI and nuScenes are preprocessed (the paper says so when explaining why L3C2 cannot run), so L and W are inferred, and tie-breaking among points sharing a (v,u) bin is unexamined. The InnovizQC non-rotational test mitigates, but doesn't close it.

None of this kills the contribution. Even at the standard model's own numbers, SerLiC beats Unicorn by 11–14% with a fraction of the parameters and credible runtime, which is a solid within-field result that deserves referee time. But a referee should send it back for a re-tabled comparison separating standard and light variants, a corrected abstract, the training protocol, and ideally code. Reading group: worth a slot, mainly as a case study in how gain columns get computed.

Referee Report

4 major / 4 minor

Summary. This paper presents SerLiC, a lossless neural coding method for LiDAR reflectance attributes. The method serializes an unordered point cloud into 1D sequences using per-point laser and azimuth indices derived from spherical coordinates, then applies a Mamba-based autoregressive entropy model with window-level and sequence-level parallelization. Experiments on KITTI, Ford, nuScenes, and InnovizQC compare SerLiC against G-PCC (RAHT and Predlift), L3C2, and Unicorn in bits per point, with additional ablation studies on context design, window size, network size, and runtime. The paper reports lower bitrates than the baselines at a fraction of the parameter count and latency.

Significance. If the reported numbers are taken at face value, SerLiC is a practical and simple contribution to LiDAR attribute compression: the device-centric serialization is intuitive, the use of Mamba gives linear decoding complexity, and the model is small and fast. The evaluation follows a standard lossless coding protocol with held-out test frames, multiple external baselines, and ablations that cover each design choice. However, the headline claims in the abstract and introduction mix two model variants and overstate the compression ratio on two of the three main datasets, so the paper's central quantitative claims need correction before the contribution can be assessed.

major comments (4)

[Table 1, Table 2, Abstract] The compression-gain percentages reported for 'SerLiC' are computed from the SerLiC-Light bpp column, not the standard SerLiC column. For KITTI, the average RAHT gain of -25.41% equals (4.88-3.64)/4.88, whereas the standard model gives (4.88-3.90)/4.88 = -20.08%; for Ford, the -22.74% vs Unicorn equals (4.97-3.84)/4.97, while standard SerLiC gives (4.97-4.29)/4.97 = -13.68%. Table 2 repeats this by listing SerLiC-Light bpp (3.57, 4.11) under the heading 'SerLiC'. Because the abstract's 'up to 22% ... using only 2% of its parameters' pairs a light-model gain with a full-model parameter count, the central efficiency claim is numerically inconsistent.
[Abstract, Section 4.3] The claim of 'over 2x volume reduction against the original reflectance data' is not supported by the reported bpp for standard SerLiC on KITTI and Ford. With the paper's own numbers, standard SerLiC achieves 7/3.90 = 1.79x on KITTI and 8/4.29 = 1.86x on Ford; only nuScenes (8/2.78 = 2.88x) exceeds 2x. Even SerLiC-Light gives 1.92x on KITTI. The claim should be qualified per dataset or recomputed with the correct model variant.
[Section 3.2, Reordering] The serialization sorts points by laser index v_i and azimuth index u_i, but points that share the same (v_i,u_i) bin are not given a deterministic ordering. Since the input point cloud is described as unordered (Section 3.1) and the decoder must reconstruct the identical sequence to decode reflectance values losslessly, the lack of a tie-breaking rule leaves the codec under-specified and potentially non-reproducible. The paper should specify how ties are resolved and verify encoder-decoder consistency.
[Section 4.1, 4.2, 4.5] The experiments do not report the angular resolution values L and W used for each dataset, nor any analysis of how sensitive the bitrate is to errors in these parameters. The serialization's physical grounding depends on these values; without this information, the robustness claim is not fully supported, and the method cannot be reimplemented from the paper alone.

minor comments (4)

[Section 3.1, Eq. (2)] The cross-entropy notation p(X_l|C*_l) does not show the autoregressive conditioning on previous reflectance values that is explicit in Eq. (9); please make the notation consistent.
[Section 4.5, Non-Rotational Adaptation] The paragraph reports gains on InnovizQC but does not describe how the scan-order serialization is adapted to a non-rotational sensor; please add the adaptation details or clearly state that the same method is applied unchanged.
[Table 1 and Table 2 captions] The column labels do not distinguish the standard SerLiC model from SerLiC-Light, which is a source of the confusion documented in the major comments; please label each model configuration explicitly in the table and caption.
[Figure 1(e)] The '1000x Smaller' and '50x Smaller' annotations are not defined in the caption; please clarify what quantities are compared (e.g., parameter count versus Unicorn).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: SerLiC's bitrate claims are empirically measured against external baselines, with no load-bearing self-citation or definitional reduction.

full rationale

The reported bitrates in Table 1 are measured on held-out test frames and compared against external codecs (G-PCC RAHT/Predlift, L3C2, Unicorn), so the central efficiency claim is an empirical measurement rather than a quantity forced by the model definition. The serialization (Eqs. 1-4) computes azimuth and elevation indices from Cartesian coordinates, and the entropy model (Eqs. 2, 9, 10) is trained by maximizing likelihood on reflectance sequences; the PMF is learned from data, not derived from the target bitrate. No load-bearing uniqueness theorem or ansatz is imported from self-citations: the cited works by the same group (Wang et al.; You et al.) appear only as baselines or prior art, and the Mamba backbone is externally cited. The only mild self-referential element is that hyperparameters such as window size, depth, and width are ablated on the same benchmark datasets, which can modestly overstate the reported numbers but is standard practice and does not constitute circularity. No fitted parameter is renamed as a prediction, and no equation reduces to its input by construction. Separately, some Table 1 gain columns appear to be computed from the SerLiC-Light column, which is a reporting-consistency concern rather than a circular-derivation concern.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper rests on standard entropy-coding math plus domain assumptions about LiDAR reflectance structure. The neural network weights are learned from training data and are not listed as free parameters; the hyperparameters that were set by hand and affect the reported bitrate are listed above.

free parameters (3)

Parallel window size = 128 (default)
Hand-chosen via ablation (Table 5) to balance bitrate (3.64 bpp) against latency and GPU memory. Larger windows give the same bpp but slower decode.
Mamba depth and dimension = 5 layers, 256 dim (full); 3 layers, 64 dim (light)
Chosen via scaling study (Table 6); more layers and dimensions improve bpp at higher cost.
Angular resolutions L and W (laser count and azimuth bins) = Dataset-dependent (e.g., 64 for KITTI/Ford, 32 for nuScenes); W not explicitly given
These discretize elevation and azimuth into scanning indices (Eq. 4). They are taken from sensor specifications, not fitted to reflectance, but if they are unknown the serialization cannot be constructed.

assumptions (5)

standard math Shannon cross-entropy lower-bounds the achievable lossless bitrate, and arithmetic coding achieves it (Eq. 2).
Standard entropy coding result, stated in Section 3.1.
domain assumption Reflectance correlations are better captured along LiDAR scan order (laser and azimuth) than along spatial proximity.
Core premise of the serialization (Section 3.2), validated only empirically on four datasets.
domain assumption Sequences from different laser beams are mutually independent for the purpose of context modeling.
Used in dual parallelization (Section 3.4); supported by the supplementary ablation in Table 7.
domain assumption Correlations beyond a window of 128 points are negligible.
Window-based parallelization (Section 3.4); ablation Table 5 shows bpp is flat for window size at least 128.
domain assumption Mamba's selective state space model can represent the needed autoregressive dependencies.
Architecture choice; no theoretical guarantee is given, only an empirical comparison with attention (Section 4.5).

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Cite this review

Pith. "Pith review of Efficient LiDAR Reflectance Compression via Scanning Serialization." pith.science (2026). https://pith.science/paper/SDTIRY7V

@misc{pith2026250509433,
  author       = {Pith},
  title        = {Pith review of: Efficient LiDAR Reflectance Compression via Scanning Serialization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SDTIRY7V}},
  note         = {Machine review of arXiv:2505.09433}
}

read the original abstract

Reflectance attributes in LiDAR point clouds provide essential information for downstream tasks but remain underexplored in neural compression methods. To address this, we introduce SerLiC, a serialization-based neural compression framework to fully exploit the intrinsic characteristics of LiDAR reflectance. SerLiC first transforms 3D LiDAR point clouds into 1D sequences via scan-order serialization, offering a device-centric perspective for reflectance analysis. Each point is then tokenized into a contextual representation comprising its sensor scanning index, radial distance, and prior reflectance, for effective dependencies exploration. For efficient sequential modeling, Mamba is incorporated with a dual parallelization scheme, enabling simultaneous autoregressive dependency capture and fast processing. Extensive experiments demonstrate that SerLiC attains over 2x volume reduction against the original reflectance data, outperforming the state-of-the-art method by up to 22% reduction of compressed bits while using only 2% of its parameters. Moreover, a lightweight version of SerLiC achieves > 10 fps (frames per second) with just 111K parameters, which is attractive for real-world applications.

Figures

Figures reproduced from arXiv: 2505.09433 by the authors.

**Figure 1.** (a-c) Reflectance plays an indispensable role in downstream tasks such as 3D object detection; (d) The proposed SerLiC establishes a new state-of-the-art by outperforming the latest compression standard G-PCC (Zhang et al., 2024b) and the learningbased work Unicorn on widely accepted datasets KITTI (Behley et al., 2019), Ford (Pandey et al., 2011), and nuScenes (Caesar et al., 2020) (the number denotes sequence n… view at source ↗

**Figure 2.** SerLiC Framework. The input 3D LiDAR point cloud is first serialized into 1D ordered point sequences, which are then divided into windows for parallel processing. For each window, a Mamba-driven autoregressive coding (MDAC) scheme is employed, which embeds scanning index (F pos i ), radial distance (F ρ i ), and prior reflectance (F x i−1) as context to generate the probability mass function (PMF) for the reflectanc… view at source ↗

**Figure 3.** Basic Mamba block and Attention block. “LN” refers to Layer Norm; σ denotes SiLU activation; ⊗ means Hadamard product; ⊕ represents element-wise addition. where F s i denotes the output of the s-th Mamba layer; DWConv represents the depth-wise convolution; σ refers to the SiLU activation function; ⊗ means Hadamard product. The initial input F 0 i is set to the token feature F token i . Let F out i be the output of t… view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Comparison of decoding latency and running memory between Attention and Mamba implementations in SerLiC. increases from 64 to 512. These results are in line with the general expectation that larger models have better capacity. However, higher complexity is required. At…

**Figure 1.** Figure 1: Visualization of samples in KITTI, Ford, and nuScenes LiDAR point cloud datasets. The color indicates the value of reflectance, ranging from blue (low) to red (high) [PITH_FULL_IMAGE:figures/full_fig_p012_1.png]

**Figure 2.** Figure 2: (a) Proposed Dual Parallelization within a frame. (b) Sequential implementation across frames. (c) Frame-level pipeline across frames. Si indicates the point sequence. Wi denotes the window. Fi denote the i-th point cloud frame. We use SerLiC (light) to show the exampl…

**Figure 3.** Figure 3: The coding speed of SerLiC w/o and w/ frame-level pipeline implementation [PITH_FULL_IMAGE:figures/full_fig_p014_3.png]

**Figure 4.** Figure 4: Configuration of L3C2 codec, where four parameters are obtained from raw LiDAR physical parameters [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]

**Figure 5.** Figure 5: Intuitive observation on reflectance characteristics in KITTI. (a) shows a holistic perspective. (b) shows zoom-in the region details. (c) further zooms in the reflectance of each sensor line for observation. The color indicates the value of reflectance, ranging from b…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression
cs.CV 2026-08 conditional novelty 6.0 of 10

A LiDAR codec that keeps the most significant range bits in a self-contained stream and encodes the rest in a FIFO stream, making any prefix of the truncatable stream decode to a deterministically coarser point cloud.

Reference graph

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