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REVIEW 4 major objections 5 minor 34 references

ChronoGS reconstructs every period of a scene — buildings, seasons, lighting — in one unified Gaussian scaffold, claiming a single jointly trained model faithfully renders each discrete period and beats static and dynamic baselines.

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A single shared Gaussian scaffold with per-period features and opacity gating reconstructs multi-period scenes better than static and dynamic baselines on a new 12-scene benchmark.

T0 review reviewed 2026-08-03 challenge →

load-bearing objection A well-built system for multi-period scene reconstruction with a new public benchmark; the synthetic results are convincing and the real-scene numbers rest on an alignment assumption the paper never quantifies. the 4 major comments →

arxiv 2511.18794 v2 pith:QHX7DSDN submitted 2025-11-24 cs.GR cs.CV

ChronoGS: Disentangling Invariants and Changes in Multi-Period Scenes

Zhongtao Wang , Jiaqi Dai , Qingtian Zhu , Yilong Li , Mai Su , Fei Zhu , Meng Gai , Shaorong Wang
show 3 more authors
Chengwei Pan Yisong Chen Guoping Wang
This is my paper
classification cs.GR cs.CV
keywords multi-period scene reconstructiontemporal consistency3D Gaussian representationanchor scaffolddisentangled representationscene evolutionbenchmark datasetnovel view synthesis
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

Multi-period scenes — the same place photographed months or years apart, with new buildings, shifting seasons, and changing light — sit between two established settings: static scenes assume one fixed geometry, dynamic scenes assume smooth motion. ChronoGS's claim is that such scenes factorize cleanly into shared canonical geometry and period-specific variation, and that this factorization can be learned jointly by a single temporally modulated Gaussian representation built on a unified anchor scaffold of union geometry. Each anchor keeps a time-invariant base feature and a pool of per-period features, and a geometry activation mechanism switches individual Gaussians on or off per period, so the same scaffold expresses both what persists and what appears or disappears. On the new ChronoScene benchmark — 12 real and synthetic scenes, 42 sub-scenes, 8,891 images — the paper reports the best scores on standard image-quality metrics (PSNR, SSIM, LPIPS) for every scene, and shows joint multi-period training beats per-period static training with less storage and fewer iterations. If correct, this gives a practical route to consistent long-term 3D reconstruction for city re-scans, construction monitoring, and environmental observation.

Core claim

The paper establishes a representation designed for scenes that are discrete in time but shared in major structure. ChronoGS builds an anchor scaffold from the union of all periods' sparse point clouds, then, for a given time t, modulates each anchor's features with a temporal encoding: a one-hot vector at observed periods, linearly interpolated between them for intermediate timestamps. A lightweight decoder predicts Gaussian shape, opacity, and color from the modulated features, while the spatial offsets of the Gaussians around each anchor are fixed and period-invariant. Geometry change is therefore expressed through visibility: Gaussians whose decoded opacity falls below zero are excluded

What carries the argument

The anchor scaffold of union geometry: a set of learnable spatial anchors, initialized from the merged sparse point clouds of all periods, each of which emits a small cluster of Gaussians. The load-bearing design choice is that each anchor's Gaussian center offsets are period-invariant, so temporal geometry changes are carried by an opacity gating mechanism that switches Gaussians on or off per period, while appearance changes are carried by a base feature plus local and global period-varying features decoded through a lightweight MLP.

Load-bearing premise

The load-bearing premise is that the per-period sparse point clouds are accurately registered into a shared coordinate frame before training; if cross-period alignment is off, the unified scaffold inherits the error, and since Gaussian center offsets are period-invariant and geometry change is expressed only through opacity gating, the model has no mechanism to correct displaced anchors.

What would settle it

Take one of the synthetic ChronoScene scenes, apply a known rigid perturbation (for example, 0.1–0.5 meters) to one period's sparse point cloud before scaffold initialization, retrain ChronoGS, and measure that period's held-out image quality against the aligned model. A sharp drop would confirm that registration accuracy is the load-bearing assumption; little or no drop would show the scaffold absorbs minor misalignment through the learned features.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • On all 12 ChronoScene scenes, ChronoGS reports the best PSNR, SSIM, and LPIPS — e.g., 25.15 dB vs 22.71 dB for the strongest baseline on Canteen, and 28.80 dB average on synthetic scenes vs 22.29 dB for the best baseline.
  • Joint training across periods in one scaffold outperforms per-period static training on the Overstreet scene (22.66 vs 22.23 and 21.87 dB) while using about a third of the storage and a third of the training iterations.
  • The linearly interpolated time encoding allows rendering at intermediate timestamps between observed periods, so the reconstructed model can synthesize plausible transitional states rather than only the discrete training periods.
  • Stable regions receive denser supervision from all periods, acting as an implicit regularizer that reduces temporal averaging, ghosting, and overfitting to single-period noise.
  • Ablations show each component — base feature, local period-varying feature, and global period-varying feature — is needed; removing any one degrades reconstruction, supporting the claimed disentanglement.

Where Pith is reading between the lines

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

  • If the registration assumption holds, the same architecture is a natural fit for unregistered or crowdsourced multi-period collections: replace the human-in-the-loop alignment with automatic registration, and the method itself needs no positional correction since it never moves Gaussian centers across periods.
  • The binary on/off visibility mechanism suggests a testable boundary: a structure that first appears midway between two observed periods may not emerge correctly, since the interpolated feature must independently produce nonzero opacity; an experiment with unevenly spaced periods would reveal whether temporal extrapolation is possible.
  • The explicit split between invariant base features and period-varying features points toward an editing interface: changing the global period feature could relight a whole period, and clamping local features could freeze a structure, without retraining the scaffold.
  • The scaffold's growth and pruning rules operate online during training, which hints at a continual-learning extension — appending a new period's captures to an already-trained model rather than retraining from all periods jointly — though the paper does not claim this.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes ChronoGS, a Gaussian-splatting-based representation for reconstructing multi-period scenes, where geometry and appearance change discontinuously across discrete time periods. The method builds a unified anchor scaffold initialized from merged per-period sparse point clouds, combines period-invariant base features with local and global period-varying features, and introduces a temporal geometry gating mechanism based on predicted opacity to activate or deactivate Gaussians per period. The authors also introduce ChronoScene, a benchmark of 12 real and synthetic scenes totaling 8,891 images across 42 sub-scenes. Experiments compare against static, in-the-wild, and dynamic baselines on per-period novel-view synthesis, reporting consistent improvements across all scenes, and include ablations of the main components as well as a per-period comparison against independently trained static models.

Significance. If the results hold, ChronoGS is a simple and effective baseline for long-term scene reconstruction with discrete changes, and the ChronoScene dataset is a useful public benchmark. The paper's strengths include a thorough evaluation with large margins over baselines on 12 scenes, a per-period comparison with static training (Table 2), ablations supporting the design choices, and the release of code and data. The main risks are the unverified cross-period registration accuracy on real scenes, a dimensionally inconsistent core equation, and a hard gating mechanism that may prevent recovery of incorrectly deactivated geometry; each is central to the method's claimed behavior and should be addressed before the results can be fully accepted.

major comments (4)
  1. [Section 3.1, Eq. (2)] The feature fusion equation is dimensionally inconsistent. e(t) is a T-dimensional vector, while g(t) is T×d_g, f_var_i(t) is T×d_v, and f_base_i is d_b, with d_g=32, d_v=16, d_b=16 (Sec. 4.1). The expression g(t)⊙e(t) + f_var_i(t)⊙e(t) + f_base_i cannot be evaluated as written because the terms have different shapes and ⊙ between a matrix and a vector is not defined. The text and Fig. 2 mention concatenation, but Eq. (2) uses addition. Please clarify the actual tensor shapes and the intended operation; this defines the MLP input and is load-bearing for the method's forward pass.
  2. [Section 3.2 and Supp. §6.3] The negative-opacity gating excludes Gaussians with α<0 from both compositing and gradient backpropagation. This is a hard mask: once a primitive's opacity becomes negative for a period, it never receives gradient to become positive again, so the model cannot recover a wrongly deactivated region. Since the opacity MLP uses tanh, negative outputs are possible. The paper does not explain how the model avoids permanent dead anchors, nor does it report statistics on inactive anchors during training. Please justify the mechanism (or provide a soft-gating alternative) and report its behavior during optimization.
  3. [Section 4.2 and Section 3.2] The real-scene results in Table 1 depend on cross-period alignment performed with GPS priors and human-in-the-loop refinement, but no alignment-error statistics or verification are reported. Because anchor positions remain fixed (Supp. §6.4) and Gaussian center offsets are period-invariant, any misregistration directly corrupts the union-geometry scaffold. Please report quantitative alignment residuals (e.g., reprojection errors or manual correspondence counts) and/or perform a sensitivity analysis by perturbing the alignment to demonstrate the method's tolerance to registration error.
  4. [Abstract and Conclusion] The paper claims 'temporally consistent reconstruction' as a central contribution, but no quantitative temporal consistency metric is reported. The evaluations are per-period reconstruction metrics; the only temporal evidence is qualitative (Fig. 7). If temporal consistency is claimed, please include a measure such as consistency of rendered stable regions across periods or a temporal LPIPS/FID. Otherwise, temper the claim or define consistency operationally.
minor comments (5)
  1. [Section 4.4] Typo: 'geometry and appearence changes ascorss periods' should be 'geometry and appearance changes across periods'.
  2. [Figure 8 caption] The caption reads 'Ground Trurh'; should be 'Ground Truth'.
  3. [Supplementary Table 5] The heading refers to 'the global feature f_var_i(t)', but f_var_i(t) is the local period-varying feature. Please correct the terminology.
  4. [Section 4.2] The 'human-in-the-loop refinement' procedure is not described concretely. Please specify the number of manual correspondences, the type of edits, and how consistency was checked across periods.
  5. [Figure 7 / Section 4.4] The intermediate-state synthesis is evaluated only qualitatively. A quantitative evaluation against a held-out intermediate frame (if available) would strengthen this exploratory claim.

Circularity Check

0 steps flagged

No significant circularity: the headline result is a held-out-view photometric comparison against external baselines; no prediction reduces by construction to a fitted input or self-citation.

full rationale

The paper's central quantitative claim is that ChronoGS outperforms existing Gaussian-based methods on the ChronoScene benchmark (Table 1). This is not circular: the evaluation protocol renders held-out test cameras within each period and computes PSNR/SSIM/LPIPS against ground-truth images, using the same train/test splits for all methods (§4.3). The photometric objective (Eq. 5) and the rendering equation (Eq. 4) are standard, and the method's per-period outputs are not defined in terms of the reported metrics. The anchor scaffold is initialized from per-period COLMAP sparse point clouds (§3.2, Supp. §6.2), but this is an architectural input, not the evaluation target; the method must still generalize to unseen views. Temporal modulation (§3.1) uses period identifiers and learnable features with a photometric loss, so no fitted parameter is renamed as a prediction. The benchmark is authored by the same team, and real-scene cross-period registration relies on GPS priors plus human-in-the-loop refinement with no reported alignment-error statistics (§4.2); this is a validation/robustness limitation, not a circular reduction, because the method's equations do not force the reported scores. Similarly, the hyperparameter ablations in Supp. Tables 4-7 are performed on scenes that also appear in the main benchmark, which is an evaluation-leakage concern, but selecting d_b/d_v/d_g does not by construction determine PSNR. The only self-citation is [23] (HuG) in the related-work survey of scalable Gaussian variants; it is not load-bearing for any derivation. No step in the claimed derivation chain reduces to its own inputs or to an unverified self-citation, so the circularity score is 0.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 4 invented entities

The central claim rests on four movable pieces: hand-chosen capacity hyperparameters (tuned on the evaluation scenes), the domain premise that multi-period content factorizes into shared + independent parts, the dataset premise that all periods register accurately into one frame, and the modeling premise that geometric change is representable as appearing/disappearing content with fixed offsets. The rendering machinery (3DGS alpha compositing, Scaffold-GS anchors) is inherited from cited prior work, not re-derived. No new physical entities are introduced; the novel latent constructs (per-period feature pools, global period feature, opacity gating) are internal to the model and only indirectly testable through held-out renders.

free parameters (4)
  • Feature dimensions (d_b, d_v, d_g) = 16, 16, 32
    Chosen by ablations on the evaluation scenes themselves (Supp. Tabs. 4-6, scenes Street1/Aerial1/Aerial2); the final reported metrics use these choices.
  • Gaussians per anchor K = 10
    Hand-selected (Supp. §6.3); not ablated.
  • Anchor growth threshold τ_g = 0.0002
    Hand-set densification threshold (Supp. §6.4) governing scaffold adaptation; no sensitivity study.
  • Training iterations = 40,000 per run
    Chosen in §4.1 to "ensure sufficient convergence"; affects all reported metrics.
axioms (5)
  • domain assumption Multi-period scenes factorize: the majority of spatial content is invariant across periods and changes are independent, allowing a shared canonical geometry plus period-specific variations
    Stated in §1 as the "key observation"; the entire anchor-base decomposition depends on it.
  • domain assumption All periods' sparse point clouds can be registered into a common world coordinate frame accurately enough for a single union scaffold
    §4.2 (GPS priors + human-in-the-loop refinement for real data; COLMAP format) and §3.2; no alignment-error analysis given.
  • domain assumption Geometric change between periods is expressible by appearance/disappearance alone: anchor positions and Gaussian offsets are period-invariant, and period-specific geometry is realized by gating opacity
    §3.1: "temporal geometry changes are mainly reflected through the Gaussians' visibility." Cannot represent moving or deforming structures.
  • standard math Scaffold-GS anchor parameterization and 3DGS alpha compositing (Eq. 4) are valid and inherited
    Adopted wholesale from [14] and [7] (§3, §6.2); treated as established prior machinery.
  • ad hoc to paper Linearly interpolating one-hot period codes yields plausible intermediate states
    Eq. (1) defines interpolation; "synthesizing plausible intermediate states" (Fig. 7) is the output of this interpolation rule with no ground-truth intermediate data.
invented entities (4)
  • Anchor scaffold of union geometry no independent evidence
    purpose: Shared backbone covering the union of geometry across all periods; each anchor spawns K Gaussians per period
    Adapted from Scaffold-GS [14]; the union-geometry variant is this paper's construct. It is internal to the model — no handle outside the paper except the model's own renders.
  • Local period-varying feature pool f_var_i(t) no independent evidence
    purpose: Per-anchor, per-period attribute modulation (local appearance/geometry changes)
    Internal latent; evidence for its role comes only from the authors' ablations on two scenes (Tab. 3).
  • Global period-varying feature g(t) no independent evidence
    purpose: Scene-level per-period effects (illumination, season)
    Internal latent; ablation evidence only, on the authors' own scenes.
  • Negative-opacity geometry activation (temporal geometry gating) no independent evidence
    purpose: Deactivate Gaussians whose decoded opacity falls below zero in a given period, expressing structural appear/disappear
    Novel mechanism (Fig. 3); implemented as exclusion from both compositing and backpropagation, i.e., a hard gate. No independent measurement; evidence is the comparative experiments.

reviewed 2026-08-03 · how reviews work

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Cite this review

Pith. "Pith review of ChronoGS: Disentangling Invariants and Changes in Multi-Period Scenes." pith.science (2026). https://pith.science/paper/QHX7DSDN

@misc{pith2026251118794,
  author       = {Pith},
  title        = {Pith review of: ChronoGS: Disentangling Invariants and Changes in Multi-Period Scenes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QHX7DSDN}},
  note         = {Machine review of arXiv:2511.18794}
}
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read the original abstract

Multi-period image collections are common in real-world applications. Cities are re-scanned for mapping, construction sites are revisited for progress tracking, and natural regions are monitored for environmental change. Such data form multi-period scenes, where geometry and appearance evolve. Reconstructing such scenes is an important yet underexplored problem. Existing pipelines rely on incompatible assumptions: static and in-the-wild methods enforce a single geometry, while dynamic ones assume smooth motion, both failing under long-term, discontinuous changes. To solve this problem, we introduce ChronoGS, a temporally modulated Gaussian representation that reconstructs all periods within a unified anchor scaffold. It's also designed to disentangle stable and evolving components, achieving temporally consistent reconstruction of multi-period scenes. To catalyze relevant research, we release ChronoScene dataset, a benchmark of real and synthetic multi-period scenes, capturing geometric and appearance variation. Experiments demonstrate that ChronoGS consistently outperforms baselines in reconstruction quality and temporal consistency. Our code and the ChronoScene dataset are publicly available at https://github.com/ZhongtaoWang/ChronoGS.

Figures

Figures reproduced from arXiv: 2511.18794 by Chengwei Pan, Fei Zhu, Guoping Wang, Jiaqi Dai, Mai Su, Meng Gai, Qingtian Zhu, Shaorong Wang, Yilong Li, Yisong Chen, Zhongtao Wang.

Figure 1
Figure 1. Figure 1: As time goes by, scenes naturally evolve — sunlight shifts, new structures emerge, seasons change, and so on. Cross-period [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: For a given camera [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the temporal geometry activation mechanism. (a). Anchor scaffold encodes the union of geome￾try across all periods, and generated gaussians with negative opac￾ity are deactivated at certain periods, enabling the model to adap￾tively represent geometry variations over time. (b). Compari￾son between Scaffold-GS and our ChronoGS trained on all peri￾ods’ images. Scaffold-GS entangles structures… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison between Scaffold-GS trained in one period [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (a). Examples from the ChronoScene dataset. Top: real-world aerial captures showing natural evolution across long temporal intervals (e.g., construction and season change). Bot￾tom: our synthetic scenes edited to simulate realistic changes with controllable geometry and appearance changes. (b). Number dis￾tribution of image and point cloud across periods for 6 real￾world and 6 synthetic scenes. Left bars s… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison on ChronoScene. We compare ChronoGS with representative baselines. Each row shows novel￾view renderings of different methods, along with ground truth. Static models trained on mixed multi-period data produce ghosting and appearance blending due to inconsistent geometry, while dynamic methods that assume continuous motion fail under the large temporal gaps, causing incorrect geometry … view at source ↗
Figure 7
Figure 7. Figure 7: Temporal evolution at a test viewpoint. Rendered views from the same camera across periods demonstrate ChronoGS’s ability to model continuous temporal changes from discontinuous data. Compared to dynamic baselines such as 4DGS that assume smooth motion, which often hallucinate intermediate structures or blend neighboring periods, ChronoGS cleanly switches between distinct time states, recovering accurate g… view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative Ablation results. Removing the base, lo￾cal time-varying, or global time-varying feature leads to degraded geometry stability, local temporal fidelity, and scene-level appear￾ance consistency, respectively, compared with the full model. disabling local temporal adaptation; w/o Global., removing the global period-varying feature g(t) thus losing global ap￾pearance coherence; and w/o Var.&Global.… view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative comparison on real scenes of ChronoScene. We compare ChronoGS with representative baselines. Each row shows novel-view renderings of different methods, along with ground truth. Static models trained on mixed multi-period data produce ghosting and appearance blending due to inconsistent geometry, while dynamic methods that assume continuous motion fail under the large temporal gaps, causing inco… view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative comparison on synthetic scenes of ChronoScene. We compare ChronoGS with representative baselines. Each row shows novel-view renderings of different methods, along with ground truth. Static models trained on mixed multi-period data produce ghosting and appearance blending due to inconsistent geometry, while dynamic methods that assume continuous motion fail under the large temporal gaps, causin… view at source ↗
Figure 11
Figure 11. Figure 11: Point cloud distribution of scene Lawncourt. 0.175 0.200 0.225 0.250 0.275 0.300 0.325 0.350 X -0.250 -0.225 -0.200 -0.175 -0.150 -0.125 -0.100 -0.075 Y XY Plane Period 0 Period 1 Period 2 0.175 0.200 0.225 0.250 0.275 0.300 0.325 0.350 X -0.96 -0.94 -0.92 -0.90 -0.88 Z XZ Plane Period 0 Period 1 Period 2 -0.250 -0.225 -0.200 -0.175 -0.150 -0.125 -0.100 -0.075 Y -0.96 -0.94 -0.92 -0.90 -0.88 Z YZ Plane Pe… view at source ↗
Figure 12
Figure 12. Figure 12: Point cloud distribution of scene Overstreet. 0.12 0.14 0.16 0.18 0.20 0.22 X 0.34 0.36 0.38 0.40 0.42 0.44 0.46 Y XY Plane Period 0 Period 1 Period 2 0.12 0.14 0.16 0.18 0.20 0.22 X -0.40 -0.39 -0.38 -0.37 -0.36 -0.35 -0.34 Z XZ Plane Period 0 Period 1 Period 2 0.34 0.36 0.38 0.40 0.42 0.44 0.46 Y -0.40 -0.39 -0.38 -0.37 -0.36 -0.35 -0.34 Z YZ Plane Period 0 Period 1 Period 2 Point Cloud Projections by P… view at source ↗
Figure 13
Figure 13. Figure 13: Point cloud distribution of scene BioBuilding. 6 [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Point cloud distribution of scene Canteen. 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 X -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 Y XY Plane Period 0 Period 1 Period 2 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 X -0.96 -0.94 -0.92 -0.90 Z XZ Plane Period 0 Period 1 Period 2 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 Y -0.96 -0.94 -0.92 -0.90 Z YZ Plane Period 0 Period 1 Period 2 Point Cloud Projectio… view at source ↗
Figure 15
Figure 15. Figure 15: Point cloud distribution of scene Incubator. -0.28 -0.26 -0.24 -0.22 -0.20 -0.18 -0.16 -0.14 -0.12 X 0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70 Y XY Plane Period 0 Period 1 Period 2 -0.28 -0.26 -0.24 -0.22 -0.20 -0.18 -0.16 -0.14 -0.12 X -0.52 -0.50 -0.48 -0.46 Z XZ Plane Period 0 Period 1 Period 2 0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70 Y -0.53 -0.52 -0.51 -0.50 -0.49 -0.48 -0.47 -0.46 Z YZ Plane Period 0 P… view at source ↗
Figure 16
Figure 16. Figure 16: Point cloud distribution of scene TriPlot. 7 [PITH_FULL_IMAGE:figures/full_fig_p017_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Point cloud distribution of scene Aerial1. -0.20 -0.15 -0.10 -0.05 0.00 X -0.10 -0.05 0.00 0.05 0.10 0.15 Y XY Plane Period 0 Period 1 Period 2 Period 3 -0.20 -0.15 -0.10 -0.05 0.00 X 0.825 0.850 0.875 0.900 0.925 0.950 0.975 Z XZ Plane Period 0 Period 1 Period 2 Period 3 -0.10 -0.05 0.00 0.05 0.10 0.15 Y 0.825 0.850 0.875 0.900 0.925 0.950 0.975 Z YZ Plane Period 0 Period 1 Period 2 Period 3 Point Cloud … view at source ↗
Figure 18
Figure 18. Figure 18: Point cloud distribution of scene Aerial2. -0.075 -0.050 -0.025 0.000 0.025 0.050 0.075 0.100 X -0.300 -0.275 -0.250 -0.225 -0.200 -0.175 -0.150 -0.125 Y XY Plane Period 0 Period 1 Period 2 Period 3 -0.075 -0.050 -0.025 0.000 0.025 0.050 0.075 0.100 X 0.84 0.86 0.88 0.90 0.92 0.94 0.96 0.98 Z XZ Plane Period 0 Period 1 Period 2 Period 3 -0.300 -0.275 -0.250 -0.225 -0.200 -0.175 -0.150 -0.125 Y 0.84 0.86 0… view at source ↗
Figure 19
Figure 19. Figure 19: Point cloud distribution of scene Aerial3. 8 [PITH_FULL_IMAGE:figures/full_fig_p018_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Point cloud distribution of scene Aerial4. 400 450 500 550 600 X -500 -450 -400 -350 -300 Y XY Plane Period 0 Period 1 Period 2 Period 3 400 450 500 550 600 X -40 -20 0 20 40 60 80 100 Z XZ Plane Period 0 Period 1 Period 2 Period 3 -500 -450 -400 -350 -300 Y -40 -20 0 20 40 60 80 100 Z YZ Plane Period 0 Period 1 Period 2 Period 3 Point Cloud Projections by Period: Street1 [PITH_FULL_IMAGE:figures/full_fi… view at source ↗
Figure 21
Figure 21. Figure 21: Point cloud distribution of scene Street1. -50 0 50 100 150 200 250 300 X -550 -500 -450 -400 -350 -300 -250 Y XY Plane Period 0 Period 1 Period 2 Period 3 -50 0 50 100 150 200 250 300 X -50 0 50 100 150 Z XZ Plane Period 0 Period 1 Period 2 Period 3 -550 -500 -450 -400 -350 -300 -250 Y -50 0 50 100 150 Z YZ Plane Period 0 Period 1 Period 2 Period 3 Point Cloud Projections by Period: Street2 [PITH_FULL_I… view at source ↗
Figure 22
Figure 22. Figure 22: Point cloud distribution of scene Street2. 9 [PITH_FULL_IMAGE:figures/full_fig_p019_22.png] view at source ↗

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This paper was first reviewed by deepseek-v4-flash on August 3, 2026.