Writing study: ComPose: A Unified Completion-Pose Framework for Robust Category-Level Object Pose Estimation

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← CVPR 2026 oral writing corpus

Huan Ren, Yihan Chen, Chuxin Wang, Nailong Liu, Wenfei Yang, Tianzhu Zhang · 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: Architecture & representation (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

≈ 8.05 page-equivalents

Main figures / tables

5 / 7

Attached appendix figures / tables

0 / 0

Attached appendix span

No boundary detected; not assumed zero

Before references / appendix: ≈ 80.5%References / intervening material: ≈ 19.5%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	≈ 1.66 pp.	16.6%
2. Related Works	3	≈ 0.64 pp.	6.4%
3. Method	3	≈ 2.46 pp.	24.6%
4. Experiments	6	≈ 2.66 pp.	26.6%
5. Conclusion	8	≈ 0.25 pp.	2.5%
References	9	≈ 1.95 pp.	19.5%

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

Attached appendix

No recognized caption labels.

Argument and organization

1. Oracle motivation and prior work

   Use complete-shape oracle evidence to motivate joint completion and pose reasoning.

2. Four-stage method and controlled comparisons

   Describe extraction, completion, geometry relations and pose fitting, then separate input modalities and ablations.

Problem, claim and contribution

An oracle experiment tests whether complete geometry could help before introducing a learned completion module; the claimed opportunity is thus more specific than generic feature enhancement.

Figure 2 and Introduction, PDF p. 2 · Main paper

Argument and section order

A task definition precedes a four-component roadmap, fixing RGB-D input and pose/size outputs before equations and shape completion details.

Section 3, PDF p. 3 · Main paper

Figures, captions and visual explanation

The main REAL275 table uses visually separated RGB-D and depth-only blocks plus an explicit shape-prior column, preventing privileged inputs from disappearing into a single ranking.

Table 1, PDF p. 6; visual inspection · Main paper

Tables, comparisons and evidence

The experiments test pose accuracy, reconstruction quality, artificial occlusion and component choices separately, connecting the completed-shape explanation to more than the headline pose score.

Tables 1–7, PDF pp. 6–8 · Main paper

Appendix and reproducibility

No separate supplement is listed for this oral in the official inventory. Implementation details and the main ablations are therefore important self-contained reproduction material in the paper.

Sections 4.1–4.4, PDF pp. 6–8; official proceedings record · Main paper

Clarity and writing conventions

Named metric thresholds and a definition of the pose mAP contract sit beside implementation settings before the performance interpretation.

Section 4.1, PDF p. 6 · Main paper

What to reuse—and what not to infer

Transferable lesson: If an architecture claims to fix missing information, first show an oracle or controlled upper bound, then test whether the proposed intermediate representation actually recovers that information.

Boundary: Keep depth-only and RGB-D rows separate, and do not imply that oracle complete-shape performance is an attainable measured result of the learned model.

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

Manuscript-wide writing scan of PDF pp. 1–10: framing, argument sequence, section organization, figure/table captions, evaluation interpretation and limitations; reference sections identified. This is a writing-focused scan, not an exhaustive technical proof or reproduction audit. Main PDF pages 3 and 6 were rendered and visually inspected.

Appendix / supplement

No separate supplemental PDF is listed in the official inventory; the writing study uses the main manuscript. Linked project demonstrations and external code were not executed.

Visually inspected PDF pages

3, 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 CVPR 2026 CVF open-access accepted manuscript; retrieved 2026-09-26 · 10 pages