REVIEW 4 major objections 3 minor 3 cited by
NGC 891 hosts a thick molecular disk (FWHM ~1.1 kpc) that can contain up to 27% of the galaxy’s total molecular gas, lifted by ordinary star-formation feedback.
Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →
T0 review · grok-4.5
2026-07-15 13:35 UTC pith:JGIV626Q
load-bearing objection Only the abstract is real for NGC 891; the body is the wrong paper, so the thick-disk CO claim is interesting but still unauditable. the 4 major comments →
Imaging the disk-halo interface of NGC 891: a 2.7 kpc-thick molecular gas disk
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
After residual error-beam removal, the vertical CO(2-1) distribution of NGC 891 is a two-component Gaussian: thin-disk FWHM ≃ 360 pc and thick-disk FWHM ≃ 1.1 kpc, with emission detected to 1.3–1.4 kpc and the thick component contributing up to 27% of the galaxy’s molecular gas mass; this shows that SF-driven feedback in a normal spiral can lift significant molecular material into the halo.
What carries the argument
Two-component Gaussian decomposition of the error-beam-cleaned IRAM 30 m CO(2-1) cube, which isolates the faint high-|z| thick-disk emission from residual single-dish sidelobes.
Load-bearing premise
The residual error-beam contribution from the IRAM 30 m telescope has been estimated and subtracted well enough that the faint high-|z| CO and the derived 27% thick-disk mass fraction are real, not leftover sidelobe artifacts.
What would settle it
An independent interferometric CO map (e.g., ALMA or NOEMA) of the same high-|z| regions that recovers the same thick-disk flux and kinematics without single-dish error beams would confirm or refute the 1.1 kpc component and its mass fraction.
If this is right
- Ordinary star-forming spirals, not only starbursts, can supply a substantial molecular reservoir to the multiphase disk–halo interface.
- Galactic-fountain models must include a cold molecular phase that can survive or reform at |z| ~1 kpc.
- Extraplanar molecular gas can contribute a non-negligible fraction of a galaxy’s total H2 budget and therefore of its baryonic mass inventory.
- Comparisons with HI, DIG and dust maps become more meaningful once the molecular thick-disk mass and kinematics are known.
Where Pith is reading between the lines
- If the thick molecular layer is common, edge-on surveys with modern single-dish or interferometric CO mapping may systematically raise the cosmic molecular-gas mass density once high-|z| emission is properly recovered.
- The survival of CO at 1 kpc height implies either rapid reformation of molecules in fountain gas or shielding by dust that also reaches those altitudes, both of which can be tested with multi-line and continuum follow-up.
- The same error-beam-cleaning approach applied to other nearby edge-ons could reveal whether the ~25% thick-disk fraction is typical or special to NGC 891.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The abstract claims that new IRAM 30 m CO(2-1) maps of the edge-on spiral NGC 891 (D = 9.5 Mpc), after careful residual error-beam subtraction, reveal a two-component vertical molecular structure: a bright thin disk (deconvolved FWHM ≃ 360 pc) and a fainter thick disk (deconvolved FWHM ≃ 1.1 kpc), with statistically significant emission to |z| ≃ 1.3–1.4 kpc and up to ~27% of the galaxy’s molecular mass in the thick component. The authors interpret this as SF-driven galactic-fountain transport of molecular gas in a non-starburst system, and compare with literature HI, DIG, and dust maps. The title phrases the result as a “2.7 kpc-thick molecular gas disk.”
Significance. If the high-|z| CO is intrinsic and the mass fraction robust, the result would be an important empirical constraint on multiphase disk–halo exchange: it would show that ordinary star-formation feedback can loft a substantial molecular reservoir well above the midplane, not only in starbursts. That would tighten links among molecular, atomic, ionized, and dust extraplanar components in a classic edge-on laboratory and would matter for baryon-cycle and fountain models. The abstract’s emphasis on error-beam control is the right observational priority for single-dish high-|z| work. Those strengths cannot be credited as demonstrated until the actual methods, residual maps, and mass conversion are available for audit.
major comments (4)
- The package under review does not contain the NGC 891 manuscript. The supplied FULL MANUSCRIPT TEXT is the unrelated robotics paper SysNav (arXiv:2603.06914). Residual error-beam model, subtraction residuals, vertical profiles, kinematic analysis, X_CO / line-ratio assumptions, and mass-fraction derivation for NGC 891 are therefore unavailable. No load-bearing claim of the abstract can be checked against methods, figures, or tables. Proper refereeing of 2603.06913 is not possible from this package.
- Abstract (error-beam paragraph and two-component fit): the central empirical claim—that residual high-|z| CO is intrinsic extraplanar gas, not sidelobe artifact—rests entirely on “a careful method to estimate and remove the residual contribution of the error beam.” Single-dish error beams routinely produce faint high-|z| wings. Without residual maps, the error-beam model, pre-/post-subtraction profiles, and a quantitative residual budget relative to the thick-disk amplitude, the deconvolved FWHM ≃ 1.1 kpc, the 1.3–1.4 kpc detection, and the ≤27% thick-disk mass fraction remain unauditable and could be instrumental.
- Title vs abstract thickness: the title states a “2.7 kpc-thick molecular gas disk,” while the abstract reports a thick-component deconvolved FWHM ≃ 1.1 kpc and significant emission only to 1.3–1.4 kpc. These numbers are not interchangeable without an explicit definition (FWHM, 2σ full width, full vertical extent, or something else). Until the correct manuscript defines “2.7 kpc-thick,” the headline claim is inconsistent with the abstract’s quantitative results and risks overstating the vertical scale.
- Abstract (mass fraction “up to 27%”): the thick-disk mass fraction depends on the two-Gaussian decomposition, the adopted α_CO (or X_CO), the CO(2-1)/CO(1-0) ratio, and how the two mapped 6×6 kpc NE patches plus center are extrapolated to the whole galaxy. None of these choices, uncertainties, or extrapolation steps are inspectable in the supplied text. The “up to 27%” figure is therefore not yet a demonstrated galaxy-wide result.
minor comments (3)
- Abstract: “statistically significant CO(2-1) emission” at 1.3–1.4 kpc needs an explicit significance criterion (e.g., per-channel S/N, integrated intensity threshold, or false-detection rate) once the correct methods section is available.
- Abstract: comparison to HI, Hα DIG, and dust is promised but not quantified here; the full paper should state which literature maps, resolutions, and vertical scales are used so the multiphase comparison is reproducible.
- Distance D = 9.5 Mpc is stated without reference; a citation and sensitivity of pc-scale FWHMs to D should appear in the methods of the correct manuscript.
Circularity Check
No circularity: purely empirical two-Gaussian fit and mass fraction from IRAM CO(2-1) data after error-beam subtraction; no derivation reduces to its own inputs by construction.
full rationale
The paper's central claims (thin/thick disk FWHMs after deconvolution, detection to 1.3-1.4 kpc, thick-component mass fraction up to 27 %, galactic-fountain interpretation) are obtained by mapping, residual error-beam removal, vertical profile fitting, and mass conversion applied to new IRAM 30 m CO(2-1) cubes, then compared with literature HI/Hα/dust maps. None of these steps is definitional of the result, none renames a fitted parameter as a prediction, and no uniqueness theorem or self-citation chain forces the outcome. Standard observational practice (Gaussian decomposition of a cleaned cube, X_CO conversion) does not constitute circular reasoning. The supplied full-text body is an unrelated robotics manuscript, so residual maps cannot be inspected, but that is an auditability/correctness issue, not circularity. Score 0 is therefore required under the hard rules.
Axiom & Free-Parameter Ledger
free parameters (3)
- CO-to-H2 conversion factor (X_CO or α_CO) and CO(2-1)/CO(1-0) line ratio
- Two-component Gaussian vertical model parameters (amplitudes, FWHMs, possibly centers)
- Error-beam residual model / subtraction amplitude
axioms (4)
- domain assumption CO(2-1) emission is a faithful tracer of molecular hydrogen in both the thin and thick disks of NGC 891.
- ad hoc to paper After error-beam removal, residual high-|z| CO is intrinsic extraplanar emission, not instrumental artifact.
- domain assumption Distance D = 9.5 Mpc for NGC 891 is accurate enough for linear scales (pc/kpc).
- domain assumption Extraplanar molecular gas is produced by SF-driven galactic fountain rather than accretion or other channels as the primary interpretation.
read the original abstract
Halos surrounding spiral galaxies act as the bridges connecting the galactic disk and the intergalactic medium (IGM). They host a significant fraction of the baryonic mass in the Universe, and feedback from star formation (SF) or active galactic nuclei (AGN) likely plays an important role in regulating this vertical baryonic component. Despite its importance, the contribution of extraplanar molecular gas remains poorly understood. We aim to characterize the vertical extent and the kinematics of molecular gas traced by CO(2-1) emission in the nearby (D = 9.5 Mpc) spiral galaxy NGC 891, one of the best studied edge-on galaxies. We also compare our results with HI, H$\alpha$-traced DIG and dust maps from the literature. Our analysis is based on new CO(2-1) observations of NGC 891 obtained with the IRAM 30m telescope. We mapped two 6 kpc $\times$ 6 kpc regions on the northeastern side and the area surrounding the galactic center. We apply a careful method to estimate and remove the residual contribution of the error beam to the CO cube. The vertical extent of the molecular gas is best described by a two-component Gaussian fit, consisting of a bright thin disk component (deconvolved FWHM $\simeq$ 360 pc) and a fainter thick disk component (deconvolved FWHM $\simeq$ 1.1 kpc). Statistically significant CO(2-1) emission is detected up to 1.3-1.4 kpc above the disk midplane. We estimate that the thick molecular disk component contains up to 27% of the total molecular gas mass of the galaxy. Our results demonstrate that SF-driven feedback in a non-starburst galaxy can lift significant amounts of molecular gas to large vertical distances. We interpret the presence of extraplanar molecular gas in NGC 891 in the framework of a galactic fountain scenario, in which material is expelled from star-forming regions and transported toward the outer halo.
Forward citations
Cited by 3 Pith papers
-
The Edge-on Galaxies in the DESI survey (EGIDE): sample building and photometry
The EGIDE project releases a tenfold larger catalogue of edge-on galaxies with griz photometry, stellar masses, redshifts and star formation rates, finding that red-sequence galaxies are thicker than blue-cloud ones a...
-
The Edge-on Galaxies in the DESI survey (EGIDE): sample building and photometry
EGIDE provides 149,215 edge-on galaxy candidates from DESI DR10 with homogeneous photometry, masses, and redshifts, ten times larger than EGIPS.
-
A CO detection in the off-plane region of the edge-on galaxy NGC 4565 with the Nobeyama 45-m telescope
CO emission is detected ~0.85 kpc above the disk of NGC 4565 at three positions, with a high-velocity component whose kinetic energy (~10^54 erg) is difficult to explain by stellar feedback alone, suggesting possible ...
Reference graph
Works this paper leans on
-
[1]
Spatialvlm: Endowing vision-language models with spatial reasoning capabilities,
B. Chen, Z. Xu, S. Kirmani, B. Ichter, D. Sadigh, L. Guibas, and F. Xia, “Spatialvlm: Endowing vision-language models with spatial reasoning capabilities,” in����������� �� ��� �������� ���������� �� �������� ������ ��� ������� �����������, 2024, pp. 14 455–14 465
2024
-
[2]
Gpt4scene: Understand 3d scenes from videos with vision-language models,
Z. Qi, Z. Zhang, Y . Fang, J. Wang, and H. Zhao, “Gpt4scene: Understand 3d scenes from videos with vision-language models,” ����� �������� ����������������, 2025
2025
-
[3]
Dd-ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames,
E. Wijmans, A. Kadian, A. Morcos, S. Lee, I. Essa, D. Parikh, M. Savva, and D. Batra, “Dd-ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames,”����� �������� ����������������, 2019
2019
-
[4]
Auxiliary tasks and explo- ration enable objectgoal navigation,
J. Ye, D. Batra, A. Das, and E. Wijmans, “Auxiliary tasks and explo- ration enable objectgoal navigation,” in����������� �� ��� �������� ������������� ���������� �� �������� ������, 2021, pp. 16 117–16 126
2021
-
[5]
Habitat- web: Learning embodied object-search strategies from human demon- strations at scale,
R. Ramrakhya, E. Undersander, D. Batra, and A. Das, “Habitat- web: Learning embodied object-search strategies from human demon- strations at scale,” in����������� �� ��� �������� ���������� �� �������� ������ ��� ������� �����������, 2022, pp. 5173–5183
2022
-
[6]
Pirlnav: Pretraining with imitation and rl finetuning for objectnav,
R. Ramrakhya, D. Batra, E. Wijmans, and A. Das, “Pirlnav: Pretraining with imitation and rl finetuning for objectnav,” in����������� �� ��� �������� ���������� �� �������� ������ ��� ������� �����������, 2023, pp. 17 896–17 906
2023
-
[7]
Uni-navid: A video-based vision-language- action model for unifying embodied navigation tasks,
J. Zhang, K. Wang, S. Wang, M. Li, H. Liu, S. Wei, Z. Wang, Z. Zhang, and H. Wang, “Uni-navid: A video-based vision-language- action model for unifying embodied navigation tasks,”����� �������� ����������������, 2024
2024
-
[8]
Object goal navigation using goal-oriented semantic exploration,
D. S. Chaplot, D. P. Gandhi, A. Gupta, and R. R. Salakhutdinov, “Object goal navigation using goal-oriented semantic exploration,” �������� �� ������ ����������� ���������� �������, vol. 33, pp. 4247–4258, 2020
2020
-
[9]
Poni: Potential functions for objectgoal navigation with interaction-free learning,
S. K. Ramakrishnan, D. S. Chaplot, Z. Al-Halah, J. Malik, and K. Grauman, “Poni: Potential functions for objectgoal navigation with interaction-free learning,” in����������� �� ��� �������� ���������� �� �������� ������ ��� ������� �����������, 2022, pp. 18 890–18 900
2022
-
[10]
3d-aware object goal navigation via simultaneous exploration and identification,
J. Zhang, L. Dai, F. Meng, Q. Fan, X. Chen, K. Xu, and H. Wang, “3d-aware object goal navigation via simultaneous exploration and identification,” 2023. [Online]. Available: https: //arxiv.org/abs/2212.00338
Pith/arXiv arXiv 2023
-
[11]
L3mvn: Leveraging large language models for visual target navigation,
B. Yu, H. Kasaei, and M. Cao, “L3mvn: Leveraging large language models for visual target navigation,” in���� �������� ������������� ���������� �� ����������� ������ ��� ������� ������. IEEE, Oct. 2023, p. 3554–3560. [Online]. Available: http://dx.doi.org/10.1109/ IROS55552.2023.10342512
arXiv 2023
-
[12]
Esc: Exploration with soft commonsense constraints for zero-shot object navigation,
K. Zhou, K. Zheng, C. Pryor, Y . Shen, H. Jin, L. Getoor, and X. E. Wang, “Esc: Exploration with soft commonsense constraints for zero-shot object navigation,” 2023. [Online]. Available: https://arxiv.org/abs/2301.13166
Pith/arXiv arXiv 2023
-
[13]
Vlfm: Vision- language frontier maps for zero-shot semantic navigation,
N. Yokoyama, S. Ha, D. Batra, J. Wang, and B. Bucher, “Vlfm: Vision- language frontier maps for zero-shot semantic navigation,” in���� ���� ������������� ���������� �� �������� ��� ���������� ������. IEEE, 2024, pp. 42–48
2024
-
[14]
Sg-nav: Online 3d scene graph prompting for llm-based zero-shot object navigation,
H. Yin, X. Xu, Z. Wu, J. Zhou, and J. Lu, “Sg-nav: Online 3d scene graph prompting for llm-based zero-shot object navigation,”�������� �� ������ ����������� ���������� �������, vol. 37, pp. 5285–5307, 2024
2024
-
[15]
Move to understand a 3d scene: Bridging visual grounding and exploration for efficient and versatile embodied navigation,
Z. Zhu, X. Wang, Y . Li, Z. Zhang, X. Ma, Y . Chen, B. Jia, W. Liang, Q. Yu, Z. Deng�� ���, “Move to understand a 3d scene: Bridging visual grounding and exploration for efficient and versatile embodied navigation,” in����������� �� ��� �������� ������������� ���������� �� �������� ������, 2025, pp. 8120–8132
2025
-
[16]
Apex- nav: An adaptive exploration strategy for zero-shot object navigation with target-centric semantic fusion,
M. Zhang, Y . Du, C. Wu, J. Zhou, Z. Qi, J. Ma, and B. Zhou, “Apex- nav: An adaptive exploration strategy for zero-shot object navigation with target-centric semantic fusion,”����� �������� ����������������, 2025
2025
-
[17]
Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation,
J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation,” 2022. [Online]. Available: https://arxiv.org/abs/2201. 12086
2022
-
[18]
Learning transferable visual models from natural language supervision,
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark�� ���, “Learning transferable visual models from natural language supervision,” in������������� ���������� �� ������� ��������. PmLR, 2021, pp. 8748–8763
2021
-
[19]
Gpt-4v (ision) is a human-aligned evaluator for text- to-3d generation,
T. Wu, G. Yang, Z. Li, K. Zhang, Z. Liu, L. Guibas, D. Lin, and G. Wetzstein, “Gpt-4v (ision) is a human-aligned evaluator for text- to-3d generation,” in����������� �� ��� �������� ���������� �� �������� ������ ��� ������� �����������, 2024, pp. 22 227–22 238
2024
-
[20]
Gem- ini: a family of highly capable multimodal models,
G. Team, R. Anil, S. Borgeaud, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millican�� ���, “Gem- ini: a family of highly capable multimodal models,”����� �������� ����������������, 2023
2023
-
[21]
Instructnav: Zero-shot system for generic instruction navigation in unexplored environment,
Y . Long, W. Cai, H. Wang, G. Zhan, and H. Dong, “Instructnav: Zero-shot system for generic instruction navigation in unexplored environment,” in��� ������ ���������� �� ����� ��������
-
[22]
Do vision-language models represent space and how? eval- uating spatial frame of reference under ambiguities,
Z. Zhang, F. Hu, J. Lee, F. Shi, P. Kordjamshidi, J. Chai, and Z. Ma, “Do vision-language models represent space and how? eval- uating spatial frame of reference under ambiguities,”����� �������� ����������������, 2024
2024
-
[23]
Hydra: A real-time spatial perception system for 3D scene graph construction and optimization,
N. Hughes, Y . Chang, and L. Carlone, “Hydra: A real-time spatial perception system for 3D scene graph construction and optimization,” 2022
2022
-
[24]
How to not train your dragon: Training-free embodied object goal navigation with semantic frontiers,
J. Chen, G. Li, S. Kumar, B. Ghanem, and F. Yu, “How to not train your dragon: Training-free embodied object goal navigation with semantic frontiers,”����� �������� ����������������, 2023
2023
-
[25]
Cows on pasture: Baselines and benchmarks for language-driven zero-shot object navigation,
S. Y . Gadre, M. Wortsman, G. Ilharco, L. Schmidt, and S. Song, “Cows on pasture: Baselines and benchmarks for language-driven zero-shot object navigation,” in����������� �� ��� �������� ���������� �� �������� ������ ��� ������� �����������, 2023, pp. 23 171–23 181
2023
-
[26]
Navigating to objects in the real world,
T. Gervet, S. Chintala, D. Batra, J. Malik, and D. S. Chaplot, “Navigating to objects in the real world,”������� ��������, vol. 8, no. 79, p. eadf6991, 2023
2023
-
[27]
Open scene graphs for open-world object- goal navigation,
J. Loo, Z. Wu, and D. Hsu, “Open scene graphs for open-world object- goal navigation,” in����� �������� �� ��������������� ������ ��� ���������� ��� ������������ �� ���� ����
-
[28]
Etpnav: Evolving topological planning for vision-language navigation in continuous environments,
D. An, H. Wang, W. Wang, Z. Wang, Y . Huang, K. He, and L. Wang, “Etpnav: Evolving topological planning for vision-language navigation in continuous environments,”���� ������������ �� ������� �������� ��� ������� ������������, 2024
2024
-
[29]
Strive: Structured representation integrating vlm reasoning for efficient object navigation,
H. Zhu, Z. Li, Z. Liu, W. Wang, J. Zhang, J. Francis, and J. Oh, “Strive: Structured representation integrating vlm reasoning for efficient object navigation,”����� �������� ����������������, 2025
2025
-
[30]
V oronav: V oronoi-based zero-shot object navigation with large language model,
P. Wu, Y . Mu, B. Wu, Y . Hou, J. Ma, S. Zhang, and C. Liu, “V oronav: V oronoi-based zero-shot object navigation with large language model,” ����� �������� ����������������, 2024
2024
-
[31]
Hierarchical open-vocabulary 3d scene graphs for language-grounded robot navigation,
A. Werby, C. Huang, M. B ¨uchner, A. Valada, and W. Burgard, “Hierarchical open-vocabulary 3d scene graphs for language-grounded robot navigation,” in����� �������� �� ��������������� ������ ��� ���������� ��� ������������ �� ���� ����, 2024
2024
-
[32]
Yolo- world: Real-time open-vocabulary object detection,
T. Cheng, L. Song, Y . Ge, W. Liu, X. Wang, and Y . Shan, “Yolo- world: Real-time open-vocabulary object detection,” in����������� �� ��� �������� ���������� �� �������� ������ ��� ������� �����������, 2024, pp. 16 901–16 911
2024
-
[33]
Yoloe: Real-time seeing anything,
A. Wang, L. Liu, H. Chen, Z. Lin, J. Han, and G. Ding, “Yoloe: Real-time seeing anything,”����� �������� ����������������, 2025
2025
-
[34]
Sam 2: Segment anything in images and videos,
N. Ravi, V . Gabeur, Y .-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. R ¨adle, C. Rolland, L. Gustafson, E. Mintun, J. Pan, K. V . Alwala, N. Carion, C.-Y . Wu, R. Girshick, P. Doll ´ar, and C. Feichtenhofer, “Sam 2: Segment anything in images and videos,”����� �������� ����������������, 2024. [Online]. Available: https://arxiv.org/abs/2408.00714
Pith/arXiv arXiv 2024
-
[35]
Tare: A hierarchical framework for efficiently exploring complex 3d environments
C. Cao, H. Zhu, H. Choset, and J. Zhang, “Tare: A hierarchical framework for efficiently exploring complex 3d environments.” in ��������� ������� ��� �������, vol. 5, 2021, p. 2
2021
-
[36]
Autonomous exploration development environment and the planning algorithms,
C. Cao, H. Zhu, F. Yang, Y . Xia, H. Choset, J. Oh, and J. Zhang, “Autonomous exploration development environment and the planning algorithms,” in���� ������������� ���������� �� �������� ��� ��� �������� ������. IEEE, 2022, pp. 8921–8928
2022
-
[37]
Openfmnav: Towards open-set zero- shot object navigation via vision-language foundation models,
Y . Kuang, H. Lin, and M. Jiang, “Openfmnav: Towards open-set zero- shot object navigation via vision-language foundation models,”����� �������� ����������������, 2024
2024
-
[38]
Trihelper: Zero-shot object navigation with dynamic assistance,
L. Zhang, Q. Zhang, H. Wang, E. Xiao, Z. Jiang, H. Chen, and R. Xu, “Trihelper: Zero-shot object navigation with dynamic assistance,” in ���� �������� ������������� ���������� �� ����������� ������ ��� ������� ������. IEEE, 2024, pp. 10 035–10 042
2024
-
[39]
Autonomy stack for mecanum wheel platform,
J. Zhang, “Autonomy stack for mecanum wheel platform,” https: //github.com/jizhang-cmu/autonomy stack mecanum wheel platform, 2024, accessed: 2025-04-29
2024
-
[40]
Habitat challenge 2023,
K. Yadav, J. Krantz, R. Ramrakhya, S. K. Ramakrishnan, J. Yang, A. Wang, J. Turner, A. Gokaslan, V .-P. Berges, R. Mootaghi, O. Maksymets, A. X. Chang, M. Savva, A. Clegg, D. S. Chaplot, and D. Batra, “Habitat challenge 2023,” https://aihabitat.org/challenge/ 2023/, 2023
2023
-
[41]
Habitat challenge 2022,
K. Yadav, S. K. Ramakrishnan, A. Gokaslan, O. Maksymets, R. Jain, R. Ramrakhya, A. X. Chang, A. Clegg, M. Savva, E. Undersander, D. S. Chaplot, and D. Batra, “Habitat challenge 2022,” https://aihabitat. org/challenge/2022/, 2022
2022
-
[42]
Habitat 3.0: A co-habitat for humans, avatars and robots,
X. Puig, E. Undersander, A. Szot, M. D. Cote, R. Partsey, J. Yang, R. Desai, A. W. Clegg, M. Hlavac, T. Min, T. Gervet, V . V ondruˇs, V .-P. Berges, J. Turner, O. Maksymets, Z. Kira, M. Kalakrishnan, J. Malik, D. S. Chaplot, U. Jain, D. Batra, A. Rai, and R. Mottaghi, “Habitat 3.0: A co-habitat for humans, avatars and robots,” 2023
2023
-
[43]
Matterport3d: Learning from rgb-d data in indoor environments,
A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, and Y . Zhang, “Matterport3d: Learning from rgb-d data in indoor environments,”������������� ���������� �� �� ������ �����, 2017
2017
-
[44]
Hm3d- ovon: A dataset and benchmark for open-vocabulary object goal navigation,
N. Yokoyama, R. Ramrakhya, A. Das, D. Batra, and S. Ha, “Hm3d- ovon: A dataset and benchmark for open-vocabulary object goal navigation,” in���� �������� ������������� ���������� �� ����������� ������ ��� ������� ������. IEEE, 2024, pp. 5543–5550
2024
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.