Writing study: Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion

ICCV overviewCorpus atlasBy contribution typeVisual galleryRules & templateWriting patternsPaper studies

← ICCV 2025 oral writing corpus

Shengyuan Zhang, An Zhao, Ling Yang, Zejian Li, Chenye Meng, Haoran Xu, Tianrun Chen, AnYang Wei, Perry Pengyun Gu, Lingyun Sun · Official ICCV 2025 oral record

Observed writing choices, not acceptance rules. This study examines the inspected manuscript version. It does not establish why the paper was accepted or that its presentation choices should be copied unchanged.

Figure, table and section ledger

Editorial contribution type: Efficiency, inference & systems (primary).

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF

10 physical pages

Before references / appendix

≈ 8.1 page-equivalents

Main figures / tables

5 / 4

Attached appendix figures / tables

0 / 0

Attached appendix span

No boundary detected; not assumed zero

Before references / appendix: ≈ 81%References / intervening material: ≈ 19%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Extraction notes: No attached appendix boundary detected. Zero means no recognized caption, not independently verified absence.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level heading	Starts on PDF page	Estimated span	Share of entire PDF
1. Introduction	1	≈ 0.86 pp.	8.6%
2. Related work	2	≈ 0.48 pp.	4.8%
3. Preliminary	3	≈ 0.95 pp.	9.5%
4. Method	4	≈ 2.21 pp.	22.1%
5. Experiment	6	≈ 2.38 pp.	23.8%
6. Conclusion	8	≈ 0.26 pp.	2.6%
Acknowledgement	9	≈ 0.06 pp.	0.6%
References	9	≈ 1.9 pp.	19%

All detected figure and table locations

Main text

• Figure 1 · p. 1
• Figure 2 · p. 2
• Figure 3 · p. 4
• Table 1 · p. 6
• Table 2 · p. 6
• Table 3 · p. 6
• Table 4 · p. 6
• Figure 4 · p. 7
• Figure 5 · p. 8

Attached appendix

No recognized caption labels.

Argument and organization

1. 1 Introduction; 2 Related work; 3 Preliminary

   Introduces diffusion latency as the obstacle, then defines point-local rather than scene-global noise.

2. 4 Method

   Explains bidirectional score distillation, scene/point structural losses and alternating student/auxiliary optimization.

3. 5 Experiment; 6 Conclusion

   Separates two datasets, loss removal, sampling-step tradeoffs and structural diagnostics before teacher-dependence limitations.

Problem, claim and contribution

The argument targets slow diffusion completion, not direct evidence that completed scans improve a deployed autonomous-driving system.

Sections 1 and 6, physical pp.1–2,8 · Main paper

Argument and section order

The LiDAR-specific noising contract precedes the generic score-distillation derivation; structural supervision is motivated as compensation for lost local detail.

Sections 3.2–4.2, pp.3–5 · Main paper

Figures, captions and visual explanation

The overview numbers seven training operations and separates frozen teacher, trainable auxiliary and student pathways, matching the later alternating optimization description.

Figure 3, p.4 · Main paper

Tables, comparisons and evidence

Comparison tables preserve refined versus unrefined variants; the step-count table shows quality deterioration at one/two steps rather than treating acceleration as a free improvement.

Tables 1–4, p.6; Section 5.2, p.7 · Main paper

Appendix and reproducibility

Implementation, loss/keypoint controls and mathematical details are routed to S1, S4 and S5; the conclusion describes semantic completion there as preliminary. Only main pointers were inspected.

Sections 4–6, pp.4,6–8 · Main paper

Clarity and writing conventions

The conclusion explicitly states teacher-model dependence and the incomplete exploration of semantic completion.

Section 6, p.8 · Main paper

What to reuse—and what not to infer

Transferable lesson: Pair an acceleration curve with a matched sampling-step control and keep auxiliary refinement visible in row labels.

Boundary: Seconds per completed scene are not real-time autonomous-perception evidence. Refined versus unrefined outputs and teacher initialization change the comparison contract; geometric distance metrics alone do not establish downstream driving safety.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-10-07

Reading status

Writing-focused manuscript scan + selected visuals inspected

Main text

Writing-oriented scan across the main manuscript, physical PDF pp.1–8, covering framing, section progression, method explanations, captions, comparison protocols and concluding scope; reference boundary checked. Selected full-page rasters were inspected. Not exhaustive line-by-line reading, proof verification or experimental reproduction.

Appendix / supplement

Main-paper appendix/supplement references were checked. A separate supplementary source is linked in the inventory but was not retrieved or inspected; its contents are not included in the reading or visual-page counts.

Visually inspected PDF pages

4, 6

This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.

• Official CVF open-access ICCV 2025 accepted-paper PDF, retrieved 7 October 2026; CVF states identical to accepted version except watermark; not assumed identical to initial submission · 10 pages