Writing study: SeeGroup: Multi-Layer Depth Estimation of Transparent Surfaces via Self-Determined Grouping

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← CVPR 2026 oral writing corpus

Hongyu Wen, Jia Deng · Official CVPR 2026 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: Training, objectives & optimization (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

11 physical pages

Before references / appendix

≈ 8.08 page-equivalents

Main figures / tables

5 / 4

Attached appendix figures / tables

0 / 0

Attached appendix span

Separate file recorded below; not in this PDF total

Before references / appendix: ≈ 73.45%References / intervening material: ≈ 26.55%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	≈ 1.46 pp.	13.27%
2. Related Work	2	≈ 1.06 pp.	9.64%
3. Method	3	≈ 1.88 pp.	17.09%
4. Experiments	5	≈ 3.2 pp.	29.09%
5. Conclusion	8	≈ 0.15 pp.	1.36%
Acknowledgments	9	≈ 0.04 pp.	0.36%
References	9	≈ 2.92 pp.	26.55%

All detected figure and table locations

Main text

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

Attached appendix

No recognized caption labels.

Separate source files—excluded from canonical-PDF totals

A separate author version may repeat main text and references. Whole-file lengths and counts are not extra appendix material and are never added to the canonical PDF totals. Read the study's source scope for which sections received substantive reading versus layout inspection.

Additional source 1

Separate PDF (or ZIP containing it): whole-file layout of 4 physical pages; 1 detected figures and 1 tables. These may include overlapping main-text material.

Detected heading	PDF page	Estimated span
1. Additional Details	1	≈ 1.75 pp.
2. Additional Results	2	≈ 0.48 pp.
References	3	≈ 1.61 pp.

• Table 1 · p. 1
• Figure 1 · p. 3

Argument and organization

1. Problem and contribution

   SeeGroup begins by defining multiple depth layers along a camera ray and showing why a fixed ordering/grouping can be restrictive. The objective is linked to the output representation instead of being introduced as an isolated mathematical improvement.

2. Argument sequence

   Multi-layer depth, recurrent decomposition and the intensity representation are explained before the permutation-invariant loss. Experiments then use tuple-wise accuracy, layer visualizations and component/cost studies to test that particular formulation.

3. Evidence and verification

   Table 1 separates all, mixed and selected-layer test cases and reports tuple-wise accuracy; later qualitative grids expose multiple predicted layers. The cost table distinguishes backbone-inclusive costs from the additional method components.

Problem, claim and contribution

SeeGroup begins by defining multiple depth layers along a camera ray and showing why a fixed ordering/grouping can be restrictive. The objective is linked to the output representation instead of being introduced as an isolated mathematical improvement.

Introduction; Figures 1–2, PDF p.2 · Main paper · Additional source 2

Argument and section order

Multi-layer depth, recurrent decomposition and the intensity representation are explained before the permutation-invariant loss. Experiments then use tuple-wise accuracy, layer visualizations and component/cost studies to test that particular formulation.

Sections 3.1–3.4 and 4 · Main paper · Additional source 2

Figures, captions and visual explanation

Figure 1 provides a ray/surface definition and explicitly attributes the reproduced drawing to prior work. Figure 2 contrasts depth-ordered with object-centric grouping using the same scene, making the design freedom visible with a minimal example.

Figures 1–2, PDF p.2 · Main paper · Additional source 2

Tables, comparisons and evidence

Table 1 separates all, mixed and selected-layer test cases and reports tuple-wise accuracy; later qualitative grids expose multiple predicted layers. The cost table distinguishes backbone-inclusive costs from the additional method components.

Table 1, PDF p.5; Figure 4; Table 4, PDF p.7 · Main paper · Additional source 2

Appendix and reproducibility

The supplementary material expands network/loss implementation and training settings and supplies extra visual results. Main-text claims that rely on a loss derivation point readers toward the appendix rather than leaving implementation implicit.

Supplementary PDF pp.1–4; main loss derivation, PDF p.5 · Main paper · Additional source 2

Clarity and writing conventions

The derivation explains both what the initial likelihood encourages and what it fails to penalize before adding the complementary component term. This reason-for-each-term narrative is clearer than presenting only the final weighted sum.

Section 3.4, PDF p.5 · Main paper · Additional source 2

What to reuse—and what not to infer

Transferable lesson: For a new loss, define the prediction unit visually, explain the unwanted solution each term prevents, and evaluate with metrics that match the structured output.

Boundary: Tuple-wise depth accuracy is not the same as a single front-surface depth metric. Keep the transparent-layer setting and synthetic/real evaluation distinctions explicit.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-09-26

Reading status

Writing-focused manuscript scan + selected visuals inspected

Main text

Writing-oriented scan throughout main PDF pp.1–8 and references pp.9–11: abstract/framing, section sequence, figure/table captions, evaluation organization and conclusions; not an exhaustive technical verification.

Appendix / supplement

Writing-oriented supplement scan: all four supplementary PDF pages, including architecture/loss implementation, training details and additional qualitative results. Video/code assets were not executed or assessed.

Visually inspected PDF pages

2, 5

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 CVPR 2026 accepted-paper PDF · 11 pages
• Official CVF supplementary PDF · 4 pages