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arxiv: 2605.17661 · v1 · pith:N27MOJ4Xnew · submitted 2026-05-17 · 💻 cs.RO · cs.CV

Mono-Hydra++: Real-Time Monocular Scene Graph Construction with Multi-Task Learning for 3D Indoor Mapping

U. V. B. L. Udugama , George Vosselman , Francesco Nex This is my paper

Pith reviewed 2026-05-20 12:02 UTC · model grok-4.3

classification 💻 cs.RO cs.CV
keywords monocular SLAMscene graphvisual inertial odometrymulti-task learningsemantic mappingindoor 3D mappingreal-time robotics
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The pith

Mono-Hydra++ builds real-time 3D scene graphs from monocular RGB and IMU data alone while matching or beating RGB-D trajectory accuracy.

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

The paper presents Mono-Hydra++, a pipeline that constructs hierarchical 3D scene graphs and metric semantic maps using only monocular RGB images plus IMU measurements. It fuses a multi-task neural network that predicts depth and semantics with a visual-inertial odometry front end, then applies sparse depth constraints, semantic masking, and volumetric fusion in the backend. This setup targets agile robots that cannot carry active depth sensors yet still need object-level and room-level understanding for exploration and interaction tasks. The authors report lower average trajectory error than the strongest RGB-D baselines on the Go-SLAM ScanNet subset and a 29.8 percent improvement on calibrated 7-Scenes, plus real-time embedded deployment.

Core claim

Mono-Hydra++ demonstrates that a DINOv3-based multi-task model for depth and semantics, combined with deep feature VIO, sparse predicted depth constraints in the pose graph, semantic masking for dynamic regions, and pose-aware temporal alignment before volumetric fusion, produces real-time monocular metric semantic maps and 3D scene graphs that achieve 1.6 percent lower average trajectory error than the strongest RGB-D baseline on the Go-SLAM ScanNet evaluation subset and 29.8 percent improvement over the strongest competing calibrated baseline on 7-Scenes.

What carries the argument

M2H-MX multi-task model supplying depth and semantic predictions that serve as sparse constraints inside the VIO-derived pose graph.

Load-bearing premise

The depth and semantic predictions from the multi-task model remain accurate enough to act as useful constraints in the pose graph without increasing overall trajectory error.

What would settle it

A measurement on the Go-SLAM ScanNet subset showing average trajectory error higher than the strongest RGB-D baseline would indicate that the added depth constraints do not deliver the claimed accuracy benefit.

Figures

Figures reproduced from arXiv: 2605.17661 by Francesco Nex, George Vosselman, U. V. B. L. Udugama.

Figure 1
Figure 1. Figure 1: System outputs and data flow of Mono-Hydra++. Monocular RGB and IMU [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: System overview of MONO-HYDRA++. Monocular RGB and IMU streams [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: M2H-MX architecture overview. An input RGB image is first processed by the [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Internal decoder modules of M2H-MX. Left: Register-Gated Mamba (RGM) [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: BinDepthHead mapping h˜d to Dˆ with adaptive bins and residual refinement. Mixed map h˜s Conv head 3 × 3 + 1 × 1 Logits Sˆ [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Depth factors with motion gating. Depth samples from M2H-MX are attached to [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Depth factors in the VIO factor graph. IMU factors link consecutive poses, [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative comparison on ScanNet scene0054. RGB-D results are on the top [PITH_FULL_IMAGE:figures/full_fig_p028_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative semantic mesh comparison on two representative ScanNet scenes. [PITH_FULL_IMAGE:figures/full_fig_p033_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative radius-based object retrieval at [PITH_FULL_IMAGE:figures/full_fig_p034_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Trajectory and mesh overview for the uHumans2 ablation. The top row sum [PITH_FULL_IMAGE:figures/full_fig_p037_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Qualitative uHumans2 Office H12 ablation example. The top row shows the [PITH_FULL_IMAGE:figures/full_fig_p037_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Loop-closure candidate diagnostic on the highly dynamic uHumans2 Office H12 [PITH_FULL_IMAGE:figures/full_fig_p038_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: ITC 2nd Floor full-loop reconstructions used in the real-world mapping test. [PITH_FULL_IMAGE:figures/full_fig_p040_15.png] view at source ↗
read the original abstract

Autonomous agile robots need more than metric geometry: they must understand objects, rooms, places, and spatial relations for search, inspection, exploration, and human robot interaction. Conventional metric maps support localization and collision avoidance, but do not provide this semantic and relational structure. 3D scene graphs address this gap by connecting geometry with object level and room level understanding. Building such representations on agile platforms remains difficult because aerial and lightweight robots operate under strict payload, power, and compute limits, making RGB-D cameras and LiDAR sensors impractical for many onboard settings. We present Mono-Hydra++, a real time monocular RGB plus IMU pipeline for indoor metric semantic mapping and hierarchical 3D scene graph construction. The system combines M2H-MX, a DINOv3 based multi-task model for depth and semantics, with a deep feature visual inertial odometry front end, sparse predicted depth constraints in the VIO derived pose graph, semantic masking for dynamic regions, and pose aware temporal alignment before volumetric fusion in the Mono-Hydra backend. On the Go-SLAM ScanNet evaluation subset, Mono-Hydra++ achieves 1.6% lower average trajectory error than the strongest RGB-D baseline in our comparison, while using only monocular RGB plus IMU input. On calibrated 7-Scenes, it improves average ATE by 29.8% over the strongest competing calibrated baseline. We further validate Mono-Hydra++ in a real ITC building deployment using RealSense RGB plus IMU and demonstrate embedded feasibility by deploying the ONNX/TensorRT FP16 M2H-MX-L perception model at 25.53 FPS on a Jetson Orin NX 16GB. These results show that Mono-Hydra++ can provide real time metric semantic mapping and scene graph construction for resource constrained robotic platforms without relying on active depth sensors.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces Mono-Hydra++, a real-time monocular RGB+IMU pipeline for indoor metric semantic mapping and hierarchical 3D scene graph construction. It integrates M2H-MX, a DINOv3-based multi-task model for depth and semantics, with a deep-feature VIO front-end, sparse predicted depth constraints in the VIO pose graph, semantic masking for dynamic regions, and pose-aware temporal alignment prior to volumetric fusion in the Mono-Hydra backend. The central claims are 1.6% lower average trajectory error than the strongest RGB-D baseline on the Go-SLAM ScanNet evaluation subset and 29.8% ATE improvement over the strongest competing calibrated baseline on 7-Scenes, plus real-time embedded feasibility at 25.53 FPS on Jetson Orin NX and validation in a real ITC building deployment.

Significance. If the results hold, the work would be significant for enabling semantic and relational scene understanding on payload- and power-constrained agile robots without active depth sensors. Credit is given for the embedded ONNX/TensorRT deployment results and the real-world building validation, which directly address practical constraints in robotics. The approach bridges metric VIO with multi-task perception for scene graphs, which is a relevant direction for indoor mapping applications.

major comments (2)
  1. Abstract: The headline claim of 1.6% lower ATE than the strongest RGB-D baseline on Go-SLAM ScanNet (and 29.8% on 7-Scenes) depends on sparse depth predictions from M2H-MX serving as useful constraints in the VIO-derived pose graph. No ablation isolating the depth constraints' contribution, no depth error statistics on the evaluation subsets, and no pose-graph residual comparisons with versus without the predictions are provided, so it remains unclear whether the observed gains originate from the depth output, semantic masking, temporal alignment, or the deep-feature VIO front-end.
  2. Evaluation section: The weakest assumption—that DINOv3-based depth predictions are accurate enough to act as useful constraints without introducing scale or bias errors that degrade trajectory accuracy—is not directly tested. Systematic monocular depth errors typical in indoor scenes could be down-weighted to near zero or actively harm the optimization; without reported depth metrics or controlled experiments on the ScanNet and 7-Scenes subsets, the source of the ATE improvements cannot be verified.
minor comments (1)
  1. Abstract: The system description would benefit from explicit cross-references to the full manuscript sections describing the M2H-MX architecture, the exact formulation of the sparse depth constraints in the pose graph, and the volumetric fusion backend.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which help clarify the validation of our depth constraint contributions. We address each major comment below and will incorporate the suggested analyses into the revised manuscript to strengthen the evaluation.

read point-by-point responses

  1. Referee: Abstract: The headline claim of 1.6% lower ATE than the strongest RGB-D baseline on Go-SLAM ScanNet (and 29.8% on 7-Scenes) depends on sparse depth predictions from M2H-MX serving as useful constraints in the VIO-derived pose graph. No ablation isolating the depth constraints' contribution, no depth error statistics on the evaluation subsets, and no pose-graph residual comparisons with versus without the predictions are provided, so it remains unclear whether the observed gains originate from the depth output, semantic masking, temporal alignment, or the deep-feature VIO front-end.

    Authors: We agree that isolating the contribution of the sparse depth constraints is important for substantiating the headline claims. The current results reflect the integrated Mono-Hydra++ pipeline, but to directly address the source of the ATE gains, the revised manuscript will add: depth prediction error statistics (RMSE, AbsRel, etc.) on the Go-SLAM ScanNet and 7-Scenes subsets; an ablation study that removes the depth constraints from the VIO pose graph while keeping other components fixed and reports the resulting ATE; and a comparison of pose-graph residuals with versus without the predicted depth terms. These additions will clarify the role of the depth output relative to semantic masking and the deep-feature front-end. revision: yes

  2. Referee: Evaluation section: The weakest assumption—that DINOv3-based depth predictions are accurate enough to act as useful constraints without introducing scale or bias errors that degrade trajectory accuracy—is not directly tested. Systematic monocular depth errors typical in indoor scenes could be down-weighted to near zero or actively harm the optimization; without reported depth metrics or controlled experiments on the ScanNet and 7-Scenes subsets, the source of the ATE improvements cannot be verified.

    Authors: We acknowledge that the assumption regarding depth prediction accuracy requires direct testing. The manuscript currently emphasizes end-to-end trajectory and mapping results, but to verify that the predictions act as useful constraints without harmful bias or scale drift, we will include the depth error metrics and controlled ablation experiments described in the response to the first comment. These will be added to the evaluation section, allowing readers to assess whether the monocular depth outputs improve or degrade the optimization on the reported datasets. revision: yes

Circularity Check

0 steps flagged

No significant circularity; system evaluation relies on external baselines and datasets

full rationale

The paper describes a composite pipeline (M2H-MX depth/semantics model + deep-feature VIO + sparse depth constraints + semantic masking + volumetric fusion) evaluated on ScanNet and 7-Scenes subsets against external RGB-D and calibrated baselines. No equations, fitted parameters, or self-citations are shown that would make the reported ATE improvements equivalent to internal definitions or prior author results by construction. The central claims remain falsifiable via the cited public datasets and competing methods.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; the central claims rest on the accuracy of a trained multi-task neural network and on the validity of using its depth outputs as pose-graph constraints, but no explicit free parameters, axioms, or invented entities are stated in the provided text.

pith-pipeline@v0.9.0 · 5893 in / 1063 out tokens · 55431 ms · 2026-05-20T12:02:27.647922+00:00 · methodology

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