Source-linked AI summary
φ-RIE: From Photorealistic Reconstruction to Interactive Environments
Runyi Yang, Deheng Zhang, Xiaoye Wang, Kanzhi Wu, Lei Sun, Ajad Chhatkuli, Kunyu Peng, Luc Van Gool, Danda Pani Paudel
TL;DR
Photorealistic 3DGS reconstructions do not by themselves support object-level interaction, including independent motion, contact, and revealing occluded surroundings. ϕ-RIE couples movable-asset construction with source removal and background completion, then renders assets from simulator poses. At fixed retention, selection and retry improve matched F1, and controlled tests show a manipulation gain over a single-generator baseline alongside a visual-fidelity trade-off.
Problem
Photorealistic 3DGS reconstructions may entangle object appearance with the background and leave hidden geometry or occluded background content unobserved, limiting object-level interaction.
Source: §I · p. 1Method
ϕ-RIE uses shared object evidence to construct and register movable assets, remove their source Gaussians, complete the exposed background, and drive Gaussian appearance with simulator poses.
Source: §I · p. 2Results
At fixed retention, matched F1 rises from 0.336 to 0.383 with selection and registration retry, while ϕ-RIE succeeds on 128/480 manipulation trials versus 73/480 for TRELLIS only, alongside a visual-fidelity trade-off.
Source: B. Quantitative Results · p. 5, D. Ablations · p. 7, §V · p. 8Takeaways & Limitations
The results support extending captured Gaussian scenes beyond static rendering to object-level interaction.
Source: §V · p. 8Policy success does not establish real-world dynamic fidelity.
Source: E. Discussions · p. 8
Abstract
from arXiv · showhide
3D Gaussian Splatting (3DGS) can reconstruct a captured scene photorealistically, but the resulting representation does not by itself support physical interaction. Robot simulation instead requires object-level change, \textit{i.e.}, objects must move independently, make contact, and reveal previously occluded surroundings. This gap arises because object appearance may remain entangled with the background, while hidden object geometry and occluded background content may be unobserved. To address this challenge, we present φ-RIE, a Gaussian-native pipeline that converts selected objects into movable simulator assets while preserving the remaining reconstruction. Our key observation is that asset construction and source removal should be coupled, \textit{i.e.}, one object identity should define the movable asset and the scene content to remove and complete. Accordingly, Scene Observation supplies shared evidence to Coupled Scene Construction, which creates registered assets and completed background Gaussians for simulator-driven rendering in an Interactive Environment. This coupling preserves unedited Gaussians while aligning visual and physical state. On 50 ScanNet++ scenes, evidence-based selection and registration retry increase matched F1 at 20\,mm from 0.336 to 0.383 at fixed retention. Further tests demonstrate asset executability, manipulation gains over a single-generator baseline, and the visual cost of conversion. Together, these results demonstrate that \name\ enables interactive scene conversion.
I. INTRODUCTION
Photorealistic 3D Gaussian Splatting does not by itself provide independently movable objects, contact geometry, or newly exposed background content. ϕ-RIE couples asset construction with source removal and background completion, then aligns visual assets with simulator motion.
I. INTRODUCTION
- Prepared-asset benchmarks provide executable structure, while real-world capture preserves actual-environment layout, clutter, and appearance.The paper identifies 3DGS as attractive for real-to-sim because of reconstruction fidelity and efficient rendering, and notes prior simulator integration.Source: §I · p. 1
- 3DGS primitives explain images rather than independently movable objects, so photorealistic reconstruction alone does not ensure separation, contact geometry, or completion of occluded regions.The cup example illustrates that object and table appearance may overlap in Gaussians while the cup base and covered tabletop remain unobserved.Source: §I · pp. 1–2
- Shared object evidence links candidate asset construction and selection to source-Gaussian removal and background completion.This pairing makes the inserted asset correspond to the appearance removed from the scene while unedited Gaussians retain captured appearance.Source: §I · p. 2
- Simulator body poses drive Gaussian assets, while completed meshes provide collision geometry without replacing their Gaussian appearance.The stated evaluation separates construction availability, fidelity, physical validity, and downstream task success.Source: §I · p. 2
- The evaluation spans real-scene construction, geometric and visual fidelity, physical validity, and manipulation performance.Construction is assessed on 50 ScanNet++ scenes, with held-out-view conversion cost, simulator export and drop tests, and controlled RoboCasa asset-replacement experiments.Source: §I · p. 2
II. RELATED WORK
Prior work covers Gaussian scene rendering, physics integration, asset generation, scene editing, and simulation benchmarks. ϕ-RIE focuses on locally converting an existing Gaussian field into movable objects and a completed background.
II. RELATED WORK
- Gaussian robotics work spans neural mapping, appearance-focused 3DGS and 2DGS, Gaussian dynamics, and simulators combining Gaussian rendering with physics.ϕ-RIE instead focuses on converting a captured Gaussian scene into independently movable objects and a completed background.Source: §II · p. 2
- Real-to-sim systems pair Gaussian backgrounds with mesh objects or bind Gaussian appearance to meshes, while other work constructs assets from video or refines single-image scenes.ϕ-RIE locally edits an existing Gaussian field using shared object evidence for alignment, selection, source removal, and background completion, preserving unrelated primitives.Source: §II · p. 2
- Image-based asset generators, segmentation, Gaussian-scene editing, inpainting, and harmonization address component tasks, but the cited generators do not jointly handle metric placement, collision preparation, and captured-appearance removal.ϕ-RIE updates scene Gaussians for background completion; optional harmonization changes rendered images only.Source: §II · pp. 2–3
III. GAUSSIAN-NATIVE SCENE CONVERSION
ϕ-RIE converts captured Gaussian scenes through scene observation, coupled construction, and simulator-driven rendering. The representation preserves background Gaussians while object assets move independently in a shared scene frame.
III. GAUSSIAN-NATIVE SCENE CONVERSION
- ϕ-RIE organizes conversion into Scene Observation, Coupled Scene Construction, and an Interactive Environment, with shared instances linking asset registration to source removal and completion.Simulator body poses drive Gaussian appearance; optional harmonization follows rendering and addresses a separate appearance issue.Source: §III · p. 3
A. Scene Representation
- The scene representation takes a Gaussian reconstruction, calibrated images, and an aligned surface; editable objects have observed samples, canonical-frame visual Gaussians, and timestamped object-to-scene transforms.The background retains source Gaussians outside removal sets, while object assets move independently.Source: A. Scene Representation · p. 3
B. Scene Observation
- Scene Observation lifts frozen SAM3 image masks onto the scene surface using first-hit ray intersections, then associates regions across views by voxel overlap.The resulting instances include surface samples, masks, and spatial bounds.Source: B. Scene Observation · p. 3
C. Coupled Scene Construction
Coupled Scene Construction uses the same object identity to guide movable-asset construction and background editing. Candidate assets are aligned and checked against observed geometry, with bounded retry for uncertain orientation.
C. Coupled Scene Construction
- The object and background branches share object identity but complete different missing content: object shape and the background exposed by source removal.A registered asset also supplies shape support for removing source Gaussians.Source: C. Coupled Scene Construction · p. 3
1) Object candidates and registered assets:
- The candidate pool combines single-view TRELLIS and multi-view ReconViaGen meshes and visual Gaussians; generation predicts missing shape while observations constrain metric placement.Alignment initializes scale from observed dimensions, searches yaw under an upright hypothesis, and refines with partial-to-complete ICP followed by scale and translation refinement.Source: 1) Object candidates and registered… · pp. 3–4
- The symmetric clipped nearest-point score penalizes unsupported candidate surfaces and unexplained observations, but measures observed agreement rather than hidden-shape correctness.Clipping at τ limits outliers and penalties on unobserved regions.Source: 1) Object candidates and registered… · p. 4
- Construction checks rank candidates using registration, scale, observation support, collision validity, isolated settling, residual, and stability; bounded retry tests alternative source-up directions if candidates fail.Retry changes the alignment hypothesis without regenerating or repairing shape; the default keeps the best numerically valid candidate, while a strict variant rejects candidates with unresolved checks.Source: 1) Object candidates and registered… · p. 4
2) Background Gaussians:
Background construction removes source Gaussians associated with the same object identity used for the registered asset, then completes the exposed region. In the interactive environment, simulator poses drive Gaussian appearance while completed meshes provide collision geometry.
2) Background Gaussians:
- Source-Gaussian removal combines proximity to the observed surface and completed asset with multi-view instance-mask votes.Surface proximity alone can miss diffuse or low-opacity object contributions; the union of the removal sets covers the observed and missing parts.Source: 2) Background Gaussians · p. 4
- Background completion fits a local support plane and refines new Gaussians against edited-image targets while keeping retained source Gaussians fixed.The planar assumption suits regions such as tabletops, but does not uniquely recover hidden surfaces; unsupported regions and failed completions are recorded.Source: 2) Background Gaussians · p. 4
1) Couple visual and physical state:
- Collision meshes determine contacts, while simulator body poses drive Gaussian appearance from a shared initial metric placement.The pose update preserves the initial body-to-asset offset without requiring the visual origin to coincide with the center of mass; shared motion does not ensure identical visual and collision surfaces.Source: 1) Couple visual and physical state · p. 4
- Scale is applied once during construction, after which body motion is rigid and cameras, bodies, and renderings use the same simulator state.Simulation-ready means loadable rigid objects with state-linked appearance, not guaranteed physical accuracy or task utility.Source: 1) Couple visual and physical state · p. 4
2) Optional appearance harmonization:
Optional post-render harmonization addresses lighting and shadow mismatch without changing the constructed scene or its dynamics.
2) Optional appearance harmonization:
- DiffusionHarmonizer modifies rendered image appearance using the current image and causal history from the same camera and episode, but does not reconstruct lighting or guarantee physically correct shadows.It leaves Gaussian parameters, collision geometry, and dynamics unchanged; raw renderings are retained to assess scene-construction quality separately.Source: 2) Optional appearance harmonization · p. 4
A. Experimental Setting
The experiments evaluate construction, physical validity, visual fidelity, and manipulation under separate settings. They use 50 ScanNet++ scenes for construction and planned RoboCasa trials for interaction.
A. Experimental Setting
- The construction study covers 1,871 object requests from 50 ScanNet++ scenes and measures candidate retention and geometry on independent matches.Construction time includes generation, registration, checks, and retries, but excludes acquisition, source-Gaussian training, and queueing.Source: A. Experimental Setting · pp. 4–5
- Construction varies candidate pool, selection, registration retry, and acceptance rule; the caption identifies these as the Table II row factors.Table II compares TRELLIS-only generation with a combined TRELLIS/ReconViaGen pool.Source: Table II · p. 5, A. Experimental Setting · p. 5
- Visual fidelity is measured with PSNR, SSIM, and AlexNet LPIPS on raw Gaussian renderings, while overall and conditional success are distinguished in Table III.Policy intervals use paired hierarchical resampling over layouts, configurations, and resets rather than treating resets as independent reconstructions.Source: A. Experimental Setting · p. 5, Table III · p. 5
- Interaction evaluation uses 48 RoboCasa configurations across eight layouts and three task families, with ten paired resets per configuration.Methods share the robot, controller, instructions, cameras, horizons, and native success tests; the scene set was previously evaluated, not newly held out.Source: A. Experimental Setting · p. 5
B. Quantitative Results
Evidence-based selection and registration retry improve matched construction geometry at fixed retention, while conversion incurs a visual-fidelity cost. Manipulation improves over a single-generator baseline, but the geometric gains do not establish additional policy gains.
Scene construction and physical validity
- With the same 1,800 retained candidates and 566 independent matches, evidence-based selection raises F1 from 0.336 to 0.348, and registration retry raises it to 0.383.Relative to fixed priority, the full constructor reduces CD by 25.1%, while construction time rises from 35.68 to 70.82 minutes per scene.Source: B. Quantitative Results · p. 5
Visual fidelity
- Conversion incurs a 1.144 dB PSNR loss on ScanNet++ in the initial state.Surfaces exposed only after object motion are not evaluated.Source: B. Quantitative Results · p. 5
Controlled manipulation
- ϕ-RIE succeeds on 128/480 planned trials versus 73/480 for TRELLIS only, a paired gain of 11.46 percentage points.Improvements occur in all three task families, but fixed priority achieves 131 successes and intervals for adding selection and then retry both cross zero.Source: B. Quantitative Results · p. 5
Quantitative Results
- Geometric gains from selection and retry do not establish additional policy gains, and geometry and policy studies use different objects.Fig. 3 illustrates the conversion sequence from source reconstruction through removal and inpainting to a registered replacement asset with assigned physical parameters.Source: B. Quantitative Results · p. 6, Figure 3 · p. 6
Controlled manipulation
- ϕ-RIE’s 352 unsuccessful planned trials include 160 without executable construction and 192 failures after execution; the original environment succeeds on 385/480 trials.The experiment evaluates registered-asset replacement under native mesh observations, separately from Gaussian-rendered demonstrations.Source: B. Quantitative Results · p. 6
C. Qualitative Results
The qualitative results illustrate how captured content becomes registered, movable assets and how objects move in the Interactive Environment. Ablations show that backend rankings depend on the evaluation metric, while input choices affect reference-scored construction quality and retention.
C. Qualitative Results
- Figure 3 illustrates that the inserted registered asset corresponds to the source appearance being removed, while completion supplies the newly exposed background.The figure’s parameter panel reports assigned physical properties.Source: C. Qualitative Results · p. 6
- Robot sequences show cup flipping and movement of a keyboard and headphones; shooting interactions also displace and rotate the objects.Frames progress left to right in Figure 4; Figure 5 shows initial configurations followed by two interaction states, with colored trails marking shots.Source: C. Qualitative Results · p. 6, Figure 4 · p. 6, Figure 5 · p. 7
- Optional Harmonizer renderings are compared with renderings without it; this post-rendering stage changes images rather than background Gaussians.It addresses appearance mismatch without changing geometry or simulator state.Source: C. Qualitative Results · p. 6, Figure 6 · p. 7
D. Ablations
- TRELLIS achieves the highest F1 and lowest CD, while SAM 3D Objects has the highest observed settling count at 16/29; no backend leads both geometry and stability.All backends pass collision import, but geometry and stability are evaluated on different populations in this single-seed comparison.Source: Table V · p. 6, D. Ablations · p. 7
- Gemini achieves the highest hole PSNR, exceeding Telea by 0.36 dB, while Telea has higher crop SSIM, lower LPIPS, and zero outside-mask error.The results assess 2D completion targets, not the reconstructed 3D background.Source: D. Ablations · p. 7, Table VI · p. 7
- Automatic-instance matched F1 changes from 0.582 to 0.553 across the compared surface inputs, while construction-surface scores reverse the independently evaluated ranking at 0.630 and 0.667.The single-image configuration retains 17.2% of requests with conditional F1 of 0.309; construction agreement is not a substitute for reference accuracy.Source: D. Ablations · p. 7, Table VII · p. 7
E. Discussions
The discussion reports interaction limits and bounds what the experiments establish. The evidence includes component tests and exploratory capture results, while several pipeline components receive qualitative rather than isolated quantitative evaluation.
E. Discussions
- Source-Gaussian removal, local 3D fill, and simulator-linked appearance have qualitative evidence but no isolated quantitative tests.Component experiments test selection, registration retry, and backend choices; the input study measures construction quality, not discovery precision or recall.Source: E. Discussions · pp. 7–8
- Contextual repair yields 65/480 successes versus 121/480 without repair, with conditional success rising from 37.8% to 54.2% as executions fall from 320 to 120.Destination reconstruction reduces successes from 45/240 to 20/240; these results expose coverage and asset-compatibility limitations.Source: E. Discussions · p. 8
- The pipeline assumes rigid objects, prior physical parameters, and locally planar fill; articulation, deformables, identified dynamics, arbitrary hidden geometry, and physical-robot transfer remain outside demonstrated scope.The exploratory TRELLIS-only RGB-video study yields seven loadable targets from 24 instances and 21/70 successes versus 53/70 for the reference, with 170 method trials unmeasured.Source: E. Discussions · p. 8
V. CONCLUSION
The conclusion presents ϕ-RIE as a Gaussian-native pipeline for converting selected objects into movable simulator assets while preserving unedited scene content. It reports improved geometry at fixed retention and manipulation gains over a single-generator baseline in controlled asset-replacement tests, alongside a visual-fidelity trade-off.
V. CONCLUSION
- ϕ-RIE couples shared object evidence for asset construction, source removal, and background completion, while simulator poses drive Gaussian appearance.Experiments report improved geometry at fixed retention and manipulation gains over a single-generator baseline in controlled asset-replacement tests, alongside a visual-fidelity trade-off.Source: §V · p. 8