Towards Physically Executable 3D Gaussian for Embodied Navigation
Authors: Bingchen Miao
Rong Wei
Zhiqi Ge
Xiaoquan sun
Shiqi Gao
Jingzhe Zhu
Renhan Wang
Siliang Tang
Jun Xiao
Rui Tang
Juncheng Li
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Gaussian Splatting; Vision-and-Language Navigation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: 3D Gaussian Splatting (3DGS), a 3D representation method with photorealistic real-time rendering capabilities, is regarded as an effective tool for narrowing the sim-to-real gap. However, it lacks fine-grained semantics and physical executability for Visual-Language Navigation (VLN). To address this, we propose **SAGE-3D** (**S**emantically and Physically **A**ligned **G**aussian **E**nvironments for **3D** Navigation), a new paradigm that upgrades 3DGS into an executable, semantically and physically aligned environment. It comprises two components: **(1) Object-Centric Semantic Grounding**, which adds object-level fine-grained annotations to 3DGS; and **(2) Physics-Aware Execution Jointing**, which embeds collision objects into 3DGS and constructs rich physical interfaces. We release **InteriorGS**, containing 1K object-annotated 3DGS indoor scene data, and introduce **SAGE-Bench**, the first 3DGS-based VLN benchmark with 2M VLN data. Experiments show that 3DGS scene data is more difficult to converge, while exhibiting strong generalizability, improving baseline performance by 31% on the VLN-CE Unseen task.

Published as a conference paper at ICLR 2026
TOWARDS PHYSICALLY EXECUTABLE 3D GAUSSIAN FOR EMBODIED NAVIGATION
Bingehen Miao Rong Wei Zhiqi Ge', Xiaoquan Sun Shigi Gao', Jingzhe Zhu', Renhan Wang", Siliang Tang', Jun Xiao', Rui Jancheenn Li
Zhejiang University, Manycore Tech Inc
Huazhong University of Science and Technology
ABSTRACT
3D Gaussian Splatting (3DGS), 3D representation method with photorealistic real-time rendering capabilities, is regarded as an effective tool for narrowing the sim-to-real gap. However, it lacks fine-grained semantics and physical exe- cutability for Visual-Language Navigation (VLN). To address this, we propose SAGE-3D (Semantically and Physically Aligned Gaussian Environments for 3D Navigation), new paradigm that upgrades 3DGS into an executable, semantically and physically aligned environment. It comprises two components: (1) Object- Centric Semantic Grounding, which adds object-level fine-grained annotations to 3DGS; and (2) Physics-Aware Execution Jointing, which embeds collision objects into 3DGS and constructs rich physical interfaces. We release Interi- orGS, containing IK object-annotated 3DGS indoor scene data, and introduce SAGE-Bench, the first 3DGS-based VLN benchmark with 2M VLN data. Exper- iments show that 3DGS scene data is more difficult to converge, while exhibiting strong generalizability, improving baseline performance by 31% on the VLN-CE Unseen task. The code is available in httpps:/sagee3d..gt
Traditional 3DGS
Ours SAGE-Bench Semantically and Physically
Executable
Aligned 3DGS VLN Benchmark
Semanntic--ligned
Physics-Aligned
InterlorGS
Semantically Labeled 3DGS Dataset
2M VLN Data
Semantic Maps
3DGS
Labels
Oojects
Collision
APi
Figure 1: Traditional 3DGS Our work. Compared with traditional 3DGS, InteriorGS pro- vides object-level 3DGS annotations across diverse indoor and outdoor scenes, including furnished homes, gyms, and concert halls well swimming pools and amusement parks. Meanwhile, SAGE-Bench contains semantically rich VLN data and detailed physical interfaces, representing semantically and physically aligned 3DGS paradigm. Specifically, InteriorGS comprises 1,000 annotated 3DGS scenes with over 554k object instances, and SAGE-Bench, the first 3DGS-base VLN benchmark, features 2M trajectory-instruction pairs alongside matching number of detailed collision bodies, laying a foundation for generalizable embodied navigation.
INTRODUCTION
Vision-and-Languarl Navigation (VLN) is core capability for Vision-Language Action (VLA) models, enabling them to follow natural language instructions and navigate complex indoor spaces WWei al., 2025 Zhang et 2024). Direct real-world training is costly and risky, mo- tivating the widely adopted sim-to-real paradigm (Qi et al. 2025 Zun Wang 2023). Reducing the
"Corresponding author; sunchengli@zju..duu.

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Published as a conference paper at ICLR 2026
Table 1: Comparisons with benchmarks for continuous navigation tasks. Here, "Instruction with Causality": tasks have causal dependencies rather than being mere "A-to-B" navigation; "Scene Geometry": whether the scene mesh is an imperfect estimate or accurate ground truth.
Benchmarks
Nom. of Task
Num. of Secnes
Scene Source
Instruction with Casuality
Scene Geonetry
3D Representation
VIN-CE Krants
4.5t
Extimated
Mesh
OVON
Yokoyama et al.
2024
53k
181
1IM3D
X
Cotmaated
Scanned Mesh
GOAT-Bemt Bench
Kisems
725L
HM3D
X
Fatimoted
Scanned
IRIR-CE
414
Estinated
Scaneed Mesh
LHPP VLN
Song al
2023
3.3k
216
HMDD
X
Excimased
Scunned Mesh
OctoNay-Baae Bennhh Cin el
45k
MP1D, HMSD
Eatimaned
Scanned Mesh
SAGF-Rench
2M
1000
InterGeeGS
Ground
Truth
3DGS-Messh Hybeid
Represcutation
resulting sim-to-real gap has driven the evolution of scene representations, from early scanned mesh reconstructions such as Matterport3D (Chang et al.] 2017) and HM3D (Ramakrishnan al. 1021), to most recently 3D Gaussian Splatting (3DGS) (erbl et al. 2023).
Compared with prior VLN work (Krantz et al., 2020 Song al. 2025) using scanned mesh re- constructions from RGB-D scans, 3DGS offers two key advantages: 1) Easier and more reliable object-level semanties. Scanned mesh reconstructed from noisy depth scans forms a single contin- uous surface that merges objects into surrounding structures, making later separation costly (Cheng al. 2025). In contrast, 3DGS represents scenes with discrete Gaussians that can be directly la- beled. 2) View-consistent and photorealistic appearance. Scanned mesh textures, stitched from sparse RGB viewpoints, often break under novel views, where incomplete coverage yields seams, stretching, or blur (Dalal et al. 2024). 3DGS instead optimizes continuous radiance field, yielding consistent, photorealistic views from any navigable position crucial for free- tree-moving navigation.
Despite these advantages, the current 3DGS is solely used for high-fidelity rendering (Wang 2024),
as shown in the upper left corner of Fig. T is unsuitable for effective application in VLN tasks due to its two significant limitations: (1) 3DGS is deficient in fine-grained object-level semanties. Existing 3DGS scenes contain only color and density information, with no instance IDs or object attributes (Li et al. 2024a). This makes it impossible to uniquely ground VLN instructions such as "go to the red chair next to the white bookshelf", and any attempt to recover object boundaries requires complex and error-prone post-processing. (2) Lack of physically executable structure. Gaussian Splatting is, by nature, a volumetric rendering technique; although recent efforts (e.g., SuGaR (Guédon & Lepetit 2024) attempt infer surface information from Gaussians, obtaining smooth surfaces remains challenging. Consequently, deriving reliable collision geometries from 3DGS is difficult, and aligning semantics with appearance is non-trivial.
In this work, we present SAGE-3D (Semantically and Physically Aligned Gaussian Environments for 3D Navigation), a paradigm that upgrades 3DGS from a purely perceptual scene representa- tion to an executable, semantically and physically aligned environment foundation for embod- ied navigation. This transformation enabled by two core components: (1) Object-Level Semantic Grounding. We sample 3DGS data from artist-created mesh scenes and create an object-level an- notated indoor dataset through careful manual labeling and double verification, thereby endowing 3DGS with fine- fine-grained semantics. Additionally, we design 2D semantie top-down map derived from 3DGS to support instruction generation and path planning. (2) Physics-Aware Execution Jointing. We introduce 3DGS-Mesh Hybrid Representation: starting from our mesh scene data, we extract collision bodies for each object as the physics layer, while using 3DGS to provide photorealistic appearance. This decoupled design preserves high-fidelity rendering through 3DGS and enables accurate physical simulation based on mesh-based collision bodies, with connectivity to rich robotics APIs. Together, these two components transform 3DGS into a practical embodied navigation environment substrate and open new avenues for future embodied intelligence research.
Building on this, we release InteriorGS, a dataset consisting of 1,000 manually object-annotated 3DGS scenes. It covers diverse indoor and outdoor scenes, including furnished indoor environ- ments, gyms and concert halls, as well as swimming pools and amusement parks, totaling over 554k object instances across 755 categories. We also introduce SAGE-Bench (Tab. D. the first fully pairs and a matching number of
JDGS-based VLN benchmark with 2M new detailed collision bodies. For data, provide a hierarchical instruction scheme that combines high-level semantic goals (especially task-causal ones like "I'm thirsty, get water from the table") with lew-level actions (e.g., "move from stool to sofa"). For evaluation, design three metrics
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SAGE-3D
Semantically and Physically Aligned
Gaussion Environments for 3D Nav
Object-Level Semontic Grounding
Physics-Awore Execution Jointing
Pit Nors Horee
Objec-fevel Arnotation
Physies Extrccion
Robot
Cotagen: Chaie choi.
Conare
InterioeGS
Collsion Body
goplet
Original
Mesten2-5 222
LOPARE
c Path Planning
Object Cullsioon biek
Chiik
3065 Mesh Hyari
Representabbnn
Instruction
Poem
Collaion Mesk
Hon Horaa Sernas
Arsets
Samantic Map
Friction
Galliion
Figure 2: Overview of SAGE-3D, which consists of two key components: (1) Object-Level Semantic Grounding, 3DGS data is annotated by expect at the object level, then be transformed into 2D semantic maps for path planning and instruction generation; (2) Physics-Aware Execution Jointing, where scene and object collision bodies are generated via convex hull decomposition, integrated into 3DGS form 3DGS-Men MMes Hybrid Representation, with extensive physics simulation interfaces.
for navigation natural continuity: Continuous Success Ratio, Integrated Collision Penalty, and Path Smoothness, to assess VI.N models from the perspective of continuous motion.
Extensive experiments SAGE-Bench yield several key insights: (1) 3DGS scene data renders faster but is harder to converge than scanned mesh data. 3DGS has per-frame rendering time of 6.2ms, outperforming scaned mesh's 16. 7ms. Yet reaching 40% Success Rate (SR) needs 160 it- erations (6.2h) for 3DGS vs. 120 iterations (4.8h) for scaned mesh-this slower convergence stems from our 3DGS data's higher demands, its richness and photorealism better mirror real-world complexity. (2) Our scene-rich, photorealistic 3DGS VLN data exhibits strong generalizabil- ity. Models trained entirely on this data achieve a significant performance improvement (31% SR increase) over baselines in unseen VIN-CE environments (Krantz et al., 2020). result driven by the data's 's alignment with real-world scenarios. (3) Our newly proposed three continuity metrics enable studying navigation's 's natural continuity, addressing gaps in conventional metrics. Our newly designed navigation natural continuity metrics reveal that conventional metrics fail to capture model issues like continuous collisions and unsmooth motion, for example, in one experiment case, our ICP (indicating continuous collisions) reaches 0.87, while the traditional collision rate is only 1.
In summary, contributions are follows:
We construct the first large-scale dataset of 1k fully furnished indoor 3DGS reconstructions with dense object-level annotations, released as InteriorGS.
We propose SAGE-3D, and physical validity, transforming into an executable environment foundation.
paradigm that augments 3DGS with semantic granularity
We build SAGE-Bench, VLN benchmark based on 3DGS with fine-grained semantics, accurate per-object physical simulation, and rich interfaces for robot embodiments.
We conduct extensive experiments based on our new paradigm and derive several novel insights in the VLN domain and validate the superiority of our newly introduced data.
SAGE-3D
In this section, we systematically introduce SAGE-3D, novel embodied learning paradigm based on 3DGS, as illustrated in Fig. 2 We first provide formal definition of this paradigm (Section 2.1). followed by introduction add fine-gra fine-grained semantic labels to 3DGS through manual annotation and the generation of 2D top-down semantic maps (Section[.22) We then utilize convex hull decom- position to extract collision bodies and construct rich physical simulation interface (Section 2.3)
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2.1 SAGE-3D PARADIGM
We propose SAGE-3D (Semantically and Physically Aligned Gaussian Environments for 3D Navi- gation), a new paradigm that uses 3DGS as the environment foundation for training and evaluating embodied agent. This paradigm upgrades 3DGS, originally used solely for photorealistic ren- dering, into an executable, semantically and physically aligned environment foundation that supports continuous
navigation and related tasks.
Formally, we define SAGE-3D as the process of transforming a Gaussian primitive set from 3DGS scene, with added semantics M and physics tb, into an executable environment:
G + M +
Eexec
where G G9 is the set of Gaussian primitives, M the semantic layer (e.g.. instance/cate- gory maps, attributes), and is the physics layer (e.g., collision bodies, dynamics). The resulting environment can be formalized as a semantics- and physies-augmented POMDP (Partially Observ- able Markov Decision Process):
SU,,SS.A0,,,22,M9
where 2/ is the instruction space, S the continuous state space, A the action space, O the multimodal observation space, and T' Z are physics-driven state transition and rendering functions.
The core goal of this paradigm to preserve the photorealistic rendering quality of 3DGS while introducing object-level semantics and physical executability, making 3DGS a viable environment foundation for training and evaluating embodied agents.
2.2 OBJECT-LEVEL SEMANTIC GROUNDING
Conventional 3DGS encodes appearance (e.g.. color, density) but lacks instance IDs object at- tributes, limiting precise object-level VLN instructions (Chen Wang. 2025 et al. 2024a). To overcome this, we release InteriorGS, manually annotated 3DGS dataset with object-level seman- tics, and introduce a 2D top-down semantic map generator to support instruction generation.
InteriorGS. We construct InteriorGS: dataset of 1 high-fidelity 3DGS scenes across diverse in- door and outdoor environments. It consists of 752 residential indoor scenes and 248 public scenes, including indoor public spaces such as concert halls and gyms, as well as outdoor venues includ- ing amusement parks and swimming pools, with double-verified object-level annotations, including object categories, instance IDs, and bounding box information. The dataset contains over 554k ob- ject instances across 755 categories, providing dense, semantically consistent, and broadly diverse foundation for training and evaluation.
InteriorGS's 3DGS data is sampled from our artist-created mesh scenes. To achieve reliable 3DGS reconstruction in occlusion-rich indoor environments, we render an average 3,000 camera views scene and use the open-source GSplat pipeline eYe al. 2025) to estimate the 3DGS parameters. The detailed sampling process is provided in Appendix B.
2D Semantic Top-Down Map Generation. Unlike scanned mesh workflows that build NavMesh (e.g., by exhaustive scene traversal Habitat) (Song et al., 2025 Krantz et al. (022). 3DGS lacks inherent semantics and discrete entities, making such representations infeasible. We therefore design a 2D semantic top-down map by projecting annotated 3D objects from InteriorGS onto the ground plane, with doors tagged by state (open closed half-open) and walls marked non-traversable. Although annotations are stored as axis-aligned 3D boxes, we refine each footprint into an irregular mask by sampling object surface points, projecting them, and taking 2D convex hull to optimize:
M₁ Fuse (Hull (p) Surl(ox)))
where Ma is the 2D mask for object og, Surf(og) is the set of sampled surface points of object OR, Hhpp the projection onto the ground plane, Hull(-) denotes the 2D convex-hull operator, and Fuse(-) merges multi-view masks into consistent footprint.
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High-fevel Instruction
Types
Novigation Notural Continuity
Add Object
1) Continuous Success Ratio
Mov the from table to booksheet
Exploration
Scenario Driven
time on
permissible corrider
Relative Relationship
Move the choil nost thet toble.
Episode Complexity
Integrated Collision Penalty distngush
Attribute- based
1) Scone Complexity
on empty rabie dining hall.
number of assets middle complex
Area based
oiyy
occasional contact
pertistent scraping
Walk
Path Smoothness
2) Path Complexity
Low-level Instruction
Tegnif patn
smoother botter
Turn loft the and bar tumn ieft again in the room to the right stand.
short
midtle
long
Figure 3: Overview of SAGE-Bench. SAGE-Bench includes a hierarchical instruction generation scheme, two major task types, two episode complexity categories, and three newly designed natural continuity metrics for navigation.
2.3 PHYYSICS-AWAR EXECUTION JOINTING
3DGS with semantics still cannot serve directly VLN environment, as it allows issues such as mesh penetration that hinder embodied learning (Yue et al. 2024b). To overcome this, we extract object-level collision geometry, derive navigable space, and provide physics simulation interface.
Physics Simulation with 3DGS-Mesh Hybrid Representation. Starting with version 5.0, Isaac Sim supports rendering 3DGS assets from USDZ files exported by 3DGUT (Wu et al. (025a). However, the imported 3DGS are appearance-only and do not carry physics. To enable physically executable scenes, take the artist-created triangle meshes of each object and apply CoACD (Wei et al. 2022) for convex decomposition, yielding per-object collision bodies. We then assemble USDA scene where the collision bodies are authored as invisible rigid shapes (driving contact and dynamics), while the 3DGS file remain visible and provide photorealistic appearance. Con- cretely, each object is instantiated parameters), where static-scenc objects default to static bodies, and a curated subset is configured as movable articulated support extended interactions. This 3DGS-Mesh Hybrid Representa- tion authoring removes the need to ray trace the artist meshes at runtime, preserves high-fidelity rendering through 3DGS, and supplies accurate collision geometry for physics.
USD prim and augmented with 4 (rigid-body and contact
Agents, Control, and Observations in Continuous Environment. The simulator exposes robot APIs for legged and wheeled ground platforms (e.g., Unitree G1 Go2 H1) and aerial robots (e.g., quadrotor UAVs). Action interfaces support both discrete commands (e.g., tu ttur/foorwrree r wd/stog and continuous control-velocics commands (v, w) for ground robots and 6-DoF velocity/attirude commands for execute in continuous environment (metric 3D space, teleportation between panoramic nodes). The environment provides synchronized RGB, depth, semantic seg- mentation, poses, and contact events, along with built-in collision detection, stuck/interpenetration monitoring, and recovery. Offline Offline-generated collision bodies are cached to accelerate loading and ensure stable, repeatable evaluation.
3 SAGE-BENCH
In this section, we introduce SAGE-Bench, the first 3DGS-based VLN benchmark, shown in Fig. B] It includes a hierarchical instruction generation scheme (..Ition[.I].. a three-axis evaluation framework (Section 3.2), and three navigation natural continuity metries (Section 3.3)
3.1 DATA GENERATION
Hierarchical Instruction Generation. To address the limitations of current bench-
marks (Zun Wang 2023). particularly the lack of tasks with causal dependencies such as
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"I'm thirsty, get water from the table", we introduce a hierarchical scheme that combines high-level semantics with low-level action primitives for more realistic navigation.
We define two levels of instructions: High-level instructions emphasize task semantics and human- oriented intent, and comprise categorics: Add Object (introducing causal objects or actions that make trajectory contextually meaningful); Scenario Driven (embedding specifie situational mo- tives that make the destination a reasonable place for execution); Relative Relationship (distinguish- ing similar nearby targets via spatial relations such "next to" "opposite"); Altribute-based (identifying a unique target using perceivable attributes like color, state, or contents); Area-based (directing the agent toward general functional area rather than a specific object). Low-level in- structions focus on control and kinematic evaluation, including primitive actions such as forward moves. Detailed design and explanation can found in Appendix C.
Low-level instructions are created by templating the start and end waypoints. High-level instructions are generated by feeding an MILM with a prompt (detailed in Appendix C) constructed from object categories, attributes, and spatial relations in the 2D semantic map.
Trajectory Generation. Using the collision bodies from Section 2.1 we construct the final navi- gation map by combining 1.2 m-height occupancy map with the 2D semantic map. Then we run based shortest-path search to generate trajectories, more details can found in Appendix A.
In total, we produce 2M new instruction instruction-traaectry trajectory pairs for VLN. We balance the data distribution and select 1,148 samples to form the SAGE-Bende test split, including 944 high-level and 204 low-level samples across 35 distinct scenes, with the remainder used for training and validation.
3.2 THREE-AXIS EVALUATION FRAMEWORK
SAGE-Bench introduces a three-axis evaluation framework that orthogonally combines task types, instruction level, and episode complexity into discrete evaluation slices.
Task Types. This axis specifies the task paradigm and input form, considering two fundamental navigation tasks: VLN and Visual Exploration. Visual Exploration aims to drive the model to ex- plore the environment as much as possible in order to test policy understanding of the environment and the safety of exploration. We select 100 scenes as the test set for Visual Exploration.
Instruction Level. This axis measures how semantic and structural complexity affects the model, and it aligned with the hierarchical instruction generation scheme described in Section 3.]
Episode Complexity. This axis quantifies task complexity, covering both scene complexity and path complexity. Scene complexity primarily refers to asset density: we define scenes with more than 376 assets as "many" and those with fewer than 184 assets "few". Path complexity considers path length: we define paths longer than 29.0 m as "long" and those shorter than 8.4 m as "short".
3.3 NAVIGATION NATURAL CONTINUITY METRIC
As new continuous navigation benchmark, to assess VLN model performance from the perspective of continuous motion, SAGE-Bench introduces three natural continuity metrics for navigation.
Continuous Success Ratio (CSR). It indicates the fraction of time the agent stays within a permis- sible corridor around the reference path. SR makes 0/1 judgment only at the endpoint, whereas CSR measures the proportion of time the agent stays within permissible corridor around the refer- ence path while satisfying task conditions, thus reflecting "goal-consistent" behavior throughout the
episode. Given trajectory of length T, let s(t) 1, pos(t) E C and task conditions satisfied 0, otherwise
where C is defined by buffering the reference path with radius Trol, then
T
1 T
CSR
(1). E=1
Integrated Collision Penalty (ICP). It measures the time-averaged collision intensity along the trajectory, capturing both the frequency and duration of contacts. Traditional collision rate (CR)

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Published as a conference paper at ICLR 2026
does not distinguish between occasional contact and persistent scraping. ICP integrates the collision intensity sequence c(t) E [0, 1] over time as a penalty:
T Σ c
ICP- = T
Path Smoothness (PS). It evaluates normalized smoothness score derived from consecutive heading-change (or curvature) magnitudes, where higher values indicate smoother paths. Smoother paths reduce abrupt turns and acceleration changes, benefiting real robot feasibility and stable plan- ning. PS is computed from the variance of consecutive heading changes:
PS = 1 T
Σ min 2=2
T
(LAOA,1). 1).
(L0
.1).
A0, = 0, 01-1,
Here Og denotes the agent's heading angle at trajectory time step t, and A0, is the change in heading between two consecutive time steps.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Baseline. Considering the current generalization capability of MLLM models (Teterwak et al.
2024
Li et al. 2025a Huang et al. 22224) Zhou et al. (2025) Li et al. (024b), we conducted evaluations
on wide range of models. (1) Closed-souree MLLMS as Agent: Includes Owen-VL-MAX (Bai
ct al. 2023). 1PT-4.1, GPT-5. (2) Open-source MILMS as Agent: Qwen2.5-VL-77 (Bai et al.
2023 IntemVL-22.58B (Zuu et al. 2025a). VnterVVL-3-83 (Chen al. 2024). Llama-3.2-111 11B. (3)
Vision-Language Models: We selected VLN models that have been widely used in recent years,
including Mavil.L (Zeng et 2024), NavGPT-2 (Zhou et al. 2024). CMA (Kantzz al. 2020).
NaVid (Zhang et al. 2024), and NaVILA (Cheng et 2025).
Evaluation Metric. (1) For the VLN task. addition to the three novel metrics we proposed
in Section 3.3 for evaluating the natural continuity of model navigation CSR, ICP, and PS
we also adopt common metrics used VLN tasks, including success rate (SR), oracle success rate
(OSR), and success weighted by path length (SPL) and Collision Rate (CR). (2) For the Visual Ex- ploration task. There are two metrics: Episode Time and Explored Areas. An episode is terminated immediately if collision occurs, and the maximum episode time is set to 120 seconds.
Implementation Details. We selected 500k "trajectory-instructtoo""" pairs from SAGE-Bench, with no overlap with the test ILA'' pre-trained model ing NaVILA-SAGE: and the other based on Navid's pre-trained model navid-7b-full-224 (denoted as Na Vadibbas,, producing NaVid-AAGE. Training details are shown in Appendix A.
We trained two models this subset: one based NaV- (denoted as NaVILA-base), produc-
4.2 RESULTS AND INSIGHTT
Overall Comparison SAGE-Bench. Tab. presents the experimental results of MLLMs and VLN models on SAGE-Bench. (1) SAGE-Bench poses a novel and challenging VLN task for current VLN models and MILMs. Except for the recent SOTA VLN model NaVILA, other mod- els achieve SR values no higher than 0.1 For instance, Vadi which achieves 0.37 SR and 0.49 OSR on VLN-CE R2R Val-Unseen, only obtains 0.15 SR and 0.17 OSR on SAGE-Bench. Simi- larly, NaVILA, which achieves 0.54 SR and 0.63 OSR on VLN-CE R2R Val-Unseen, records only 0.39 SR and 0.47 OSR on SAGE-Bench. (2) MILMS multimodal understanding inherently gives them some VLN capability. Both the latest open-source and closed-source MILMs achieve VLN SRs ranging from 00 to O. on SAGE-Bench comparable to dedicated VLN models such as CMA 13 SR) and Vid (0.15 15555 SR), and even surpass VLN models in OSR. For example, the 0.20 OSR achieved by VL-3 exceeds that of Vidid (0.17 OSR). Notably, several baseline models with weak VLN performance (SR < 0.20) fail to understand navigation instructions or environmen- tal information in our challenging tasks, behaving like "random or single-action prediction" " (c.g., continuous straight movement), rendering their CR, ICP, and PS metrics non-comparable.
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Comparison of different models on VLN and Visual Exploration tasks on SAGE-Bench. Bold values represent the best performance across all methods. Gray values indicate that these metries lack comparative significance due the low navigation performance the models.
Table 2:
(High-level Instruction)
Visual Exploration
Methods
SR
OSR T
SPL 1
CR L
CSR
ICP
PS
Episode Time
Explored Areas
Closed-sonee -oourre MLLMss Agent
-M-X-V
0.14
0.25
0.12
0.85
0.21
0.41
0.79
64,74
6.40
GPT-4.1
0.13
0.21
0.12
0.72
0.19
0.35
0.81
67.70
3.00
GPT-5
0.12
0.18
0.11
0.63
0.18
0,24
0.86
64.60
2.16
Open- souree ML..Ms as Agent
Qwen2.5-VI.-78
0.13
0.14
0.13
0.71
0.21
0.27
0.87
42,19
6.88
IntemVL-22.-88B
0.10
0.13
0.10
0.52
0.14
0.33
0.88
28.82
4.28
IntemmVL-3-8B
0.12
0.20
0.11
0.64
0.17
0,32
0.82
34.70
6,34
Lluma-3.2-11B
0.13
0.18
0.14
0.74
0.16
0.29
0.83
38.45
6.68
Vision anguuge Model
NaviLLM
0.05
0.06
0.05
0.21
0.09
0.24
0.90
18.73
5.74
NavGPT-2
0.10
0.12
0.11
0.33
0.14
0.29
0.83
24.51
3.36
CMA
0.13
0.15
0.14
0.54
0.26
0.28
0.86
44,26
3.22
NaVid
0.15
0.17
0.15
1.24
0.29
0.33
0.89
56,13
4.28
NaVILA
0.39
0.47
0.34
3.28
0.48
0.61
0.68
77.82
8.40
NaVid-base
0.10
0.13
0.10
0.33
0.15
0.28
0.84
20.37
3.42
Vid-SAG (Ours)
0.36
0.46
0.32
2.12
0.48
0.66
0.54
60.35
5.66
NaVIL.A-bar
0.21
0.26
0.22
3.53
0.33
0.72
0.41
58.26
6.52
NaVILA-SAGE (Ours)
0.46
0.55
0.48
2.67
0.57
0.54
0.74
82.48
8.74
Table 3: Rendering speed and training convergence comparison.
Favironment Type
Avg. Render
Arg.
SIl:40%
Scamol Mesh (MPI)HMM))
167
850
120
4.8
IDGS-Mee Hybrid Reprosttation (Chm)
62
220
160
6.2
SEAR
CR=1 ICP 0.87 PS 0.42 Case
CR=1 0.65 0..5
=R=2 ICP PS
Case 2
Case 3
Figure 4: Visualization case study of navigation natural continuity. The red trajectory the ground truth, and the blue Trajectory is the trajectory of NaVILA.
Insight 1: 3DGS
data renders fasteer than scanned mesh data but is harder converge. We randomly selected 10k training samples and 1k validation samples from both traditional scanned mesh data and our 3DGS data, and conducted experiments with the NaVILA-base model on an NVIDIA H20 GPU. Tab. compares the rendering speed and model convergence between scanned mesh VIN data and our 3DGS VI.N data. The results show that 3DGS scene data achieves per- frame rendering time of 6.2 ms and average memory usage of 220 MB, outperforming the 16.7 ms and 850 MB of scanned mesh data. However, training, to reach the same 40% SR, the 3DGS- based model required about 160 iterations and 6.2 hours, while the scanned needed only about 120 iterations and 4.8 hours. This indicates that although 3DGS scene data offers faster rendering, it presents greater training difficulty and is relatively harder to converge.
model
Insight 2: 3DGS scene data exhibits strong generalizability. To evaluate the effectiveness of our novel 3DGS-DGSaaeee based scene data, we tested the LaVILA-SAGE. and Na VaV--SAGR models, which were
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Table 4: Results on VLN-CE.
Table 5: Results on different instruction levels.
R2R Val-Unseen
SAGE-Bench VLN
Methods
Methods
Instruction Level
SR
OSR
SPL
SR
OSR
SPL 7
CSR
ICP.
PS
Seq2Seq
0.25
0.37
0.22
Low-level
0.22
0.37
0.19
0.27
0.60
0.70
GPT-4.1
Navid-base
0.22
0.32
0.17
High-level
0.13
0.21
0.12
0.19
0.35
0.81
Navid-SAGE. (Ours)
0.31
0.42
0.29
Low-level
0.20
0.35
0.18
0.26
0.61
0.69
IntermVL-3-88
CMA
0.32
0.40
0.30
High-level
0.12
0.20
0.11
0.17
0.32
0.82
NoVid
0.37
0.49
0.36
Low-level
0.24
0.42
0.21
0.34
0.63
0.64
NaVid
NaVILA-basee
0.29
0.38
0.27
High-love
0.15
0.17
0.15
0.29
0.33
0.89
NaVILA-SAGE (Ours)
0.38
0.51
0.36
NaVILA
Low-level
0.56
0.66
0.50
0.58
0.48
0.75
NaVILA
High-tevel
Table 6: Impact of the number of scenes and samples model performance.
Number Scenes
400
800
0.40
Fixed 400 scenes, verying semples
0.38
Fixed 60k samples, varying scenes
Data A Train
SAGE-Bench VLN
Success Rate (SR)
0.35
#Seenes
#Samples
SR
OSR
SPL.
CSR
ICP
PS
0.33
800
0.30
800
0.40
0.43
0.48
0.62
800
60k
0.36
0.42
0.38
0.46
0.64
0.58
400
120k
0.34
0.39
0.35
0.44
0.67
0.54
0.26
400
60k
0.31
0.37
0.33
0.43
0.67
0.52
0.23
400
30k
0.28
0.35
0.31
0.43
0.69
0.49
400
0.25
0.46
0.20
30k
120k
0.47
Number of Samples
100
60k
0.23
0.29
0.26
0.38
0.71
0.44
Figure 5:
Model performance change curve (number of scenes vs, sample size).
NaVILA base
0.21
0.26
0.22
0.36
0.72
0.41
trained solely on our BAGE-Bennde dataset, on the VLN-CE benchmark. As shown in Tab models trained entirely on SAGE-Bench data (without any VLN-CE data) achieved clear performance im- provements over their respective baselines. For example, NaVILA-SAGE achieved 31% relative SR improvement on R2R Val-Unseen (from 0.29 to 0.38) and 34% relative OSR improvement (from 0.38 to 0.51), with similar gains observed for the NaVid model.
Insight 3: Our newly proposed three continuity metries enable effective study of navigation's natural continuity, filling key gaps left by conventional metries. In Tab. 2 we report results for three navigation natural continuity metrics. We observe that CSR is generally higher than SR, indicating a more inclusive and robust metric that does not require the model to fit the ground-truth trajectory exactly. For ICP and PS, although NaVILA attains relatively high task completion (0.39 SR, 0.47 OSR), it lacks natural motion continuity: an ICP of 0.61 indicates sustained collisions during navigation, and PS of 0.68 reflects large, mechanical turning angles rather than smooth, natural motion. Additional visual examples in Fig. corroborate this finding: the NaVILA model (blue trajectory) exhibits unsmooth movement and persistent collisions that conventional metrics fail to reveal. For instance, in Case 1, the model hugs the wall for a long period, yet the collision rate CR is only 1, while our ICP reaches 0.87.
4.3 MORE FINDINGS
High-level Instructions vs. Low-level Instructions. Tab. 5 compares the performance of dif- ferent models on high-level and low-level instructions in the VLN task. Compared with low-level instruction data, which are composed of basic step-by-step actions that guide the model gradually through the task, VLN models perform worse when executing high-level instructions. Even the re- cent SOTA model Na VILA achieves only a 0.39 success rate on high-level instructions, significantly lower than its 0.56 success rate on low-level instructions. Notably, high-level instructions, with their more natural semantics, are closer to real-life scenarios, presenting greater challenges for the future development of VLN models.
Number of training Scenes vs. Training Sample Size. Tab. 6 and Fig. illustrate the influence of varying the number of scenes and the number of samples. We observe that increasing the number of scenes in the training data, while keeping the sample size constant, yields greater performance gains

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Performance by Instruction Type
Performance by Trajectory Langth
Performance by Scene Complexity
0.44
0.25
0.41
0.57
6.29
0.11
0.14
8:12
0.10
Iratruction Typa
Trajectory Length
Scate Complectyy
Figure 6: Results under Different Evaluation Slice.
than merely increasing the number of samples. Specifically, with the same number of augmented scenes (800), increasing the sampling density progressively improves the VLN model's performance on the val-unseen split. Conversely, generating the same number of samples (700k) from a larger number of environments produces better results. These findings indicate that the number of scenes (Scenes) has greater impact than the number of samples (Samples), suggesting that diversity of environments is more critical for learning VIN.
Results under Different Evaluation Slice. Based on our three-axis evaluation framework, we further present experimental results in Fig. 6 for different high-level instruction types, trajectory lengths, and scene complexities. The results show that VLN models perform worse on the "Relative Relationship" and "Attribute-based" instruction types, with SR scores for both NaVILA and VaVid more than 2% lower than those for other types. addition, as trajectory length increases and scene complexity grows, model performance drops significantly.
RELATED WORK
Vision- aadtt---n---annn Language Navigation (VLN) was first introduced by (Anderson et al. 2018 early Matterport3D-bare discrete panoramic graphs, later extended to multilingual / longer-horizon set- tings by Ku et al. (2020) and remote object grounding by Qi al. (2020); research shifted to continuous control with (Krantz. et al.) 2020) (VLN-CE) on Habitat (Savva et al.] 2019), though mainstream benchmarks still rely on scan-mesh reconstructions (with texture/semantic limitations). scenes efficiently via anisotropic Gaussian primitives for photorealistic real-time rendering has been integrated into embodied learning, such as cou- pling with MuJoCo/Isaac Sim (Jia et al., 2025 Zhu et al. (025b), adopting dual-represennattinn (Gaussians for rendering, meshes for collision) (Lou al. [025 Wu et 20256), and enhancing with lighting estimation (Phongthawee et al. 2024); however, native 3DGS lacks object-level se- mantics, needs cumbersome manual appearance/physies alignment, and struggles with precise VLNN language grounding (Krantz et al. 2020; Savva et al. 2019).
3D Gaussian Splatting
CONCLUSION
We presented SAGE-3D, paradigm that upgrades 3D Gaussian Splatting from purely perceptual scene representation to an executable, semantically and physically aligned environment foundation for embodied navigation. We release InteriorGS, the first large-scale dataset of 1K fully furnished indoor 3DGS reconstructions with dense object-level annotations, which enables robust semantic grounding in photorealistic environments. By unifying InteriorGS with a physics-aware execution layer and a hierarchical instruction-evallation benchmark, SAGE-Bench, our framework provides coherent pipeline from high-fidelity data generation to physically valid evaluation. We expect SAGE-3D to serve as foundation for future research in richer multi-step and semantic-aware navi- gation tasks, interactive manipulation, and broader sim-to-real studies.
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ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (62436007), Na- tional Key Research and Development Program of China (20252D0123100), the Zhejiang NSF (LQK26F020001), Key R&D Program of Zhejiang (2025C01001), Fundamental Research Funds for the Central Universities (226-2025-0005) Ningbo Yongjiang Talent Introduction Programme (2024A-401-G), Zhejiang University Education Foundation Qizhen Scholar Foundation.
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A APPENDIX
OVERVIEW
This the Appendix for the paper "Towards Physically Executable 3D Gaussian for Embodied Navigation". In this supplementary material we present:
The implementation details of trajectory generation and training are provided in Section
The detailed InteriorGS data sampling and construction is described in Section B
The more specific explanation of the hierarchical instruction system and prompts are pre- sented in Section
The comparison of our 3DGS-Mesh Hybrid Representation data with traditional Matterport 3D data is illustrated in Section D
The more visualizations of InteriorGS scenes are shown in Section
The visualization of the data distribution our InteriorGS is presented Section
A IMPLEMENTATION DETAILS
Trajectory Generation. We run A shortest-path search to generate trajectories with cost function that integrates free-space distance, narrow-passage penalties, and area preferences to ensure both obstacle avoidance and task feasibility. To diversify the dataset, start-end pairs are sampled across different rooms, functional areas, and object instances, and a minimum safety distance is enforced to avoid overly close viewpoints that would reduce the richness of the visual signal.
Training. We selected 500k "trajectory-instruction pairs from SAGE-Bench, with no overlap with the test set. We trained two models on this subset: one based on NaVILA's pre-trained model viwila-sglip-lllmaa------- -.-perr.n (denoted as NaVILA-base). producing NaVILA-SAGE; and the other based on Navid's pre-trained model navid-7b-full-224 (denoted NaVid-base) producing Na VAd- Training was carried out on NVIDIA Tesla H20 GPUs with a batch size of 256 and a learning rate of 2 10-5 The training data did not include any VLN-CE R2R or RxR samples.
B DETAILED SAMPLING METHOD OF INTERIORGS
To obtain reliable 3D Gaussian Splatting (3DGS) reconstructions in occlusion-rice indoor settings, we render on average 3,000 camera views per scene with ray tracing renderer and estimate 3DGS parameters using the renderer-provided poses via the open-source gsplat at pipeline. To mitigate undersampling, we employ two complementary camera placement policies:
(1) Perimeter-aware floorplan sweeps ("surround"). For each room polygon P, we generate m inwardly offset polygons {P(5) )- according to prescribed distance schedule, and allocate a global camera budget n across polygons proportionally to their perimeters. Along each p()), cameras are uniformly spaced with optical axes aligned the inward edge normals. At every place- ment, we instantiate three tangential baselines (left / center / right) and three vertical tiers: outer lower 150 mm above the floor pitched +30 (up), middle mid mei-height with 0 pitch, and upper at 500 mm below the ceiling pitched -30% (down); interior tiers (j > 1) heihttss are inter- polated between the corresponding outer tiers, with upper tiers pitched 15., lower tiers +15% and the middle tier matching the outer middle.
(2) Colume-uniform sampling. We distribute the global camera budget across rooms in proportion to room volume to favor coverage in smaller compartments, then draw 3D positions via Poisson- disk sampling for space-filling uniformity. At each sampled position, six cameras with canonical yaw-pitch templates instantiated, and a shared small random perturbation is applied to their ori- entations. Together, these policies emphasize inward- facing, depth-aware viewpoints that broaden coverage and reduce undersampling-indueed 3DGS underfitting.
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To select viewpoints appropriate distances from mesh surfaces, Figure 7 presents the camera- sampling outcomes: viewpoints shown in green are retained as the final selections, whereas those in red are discarded for being too close to the nearest mesh surface (below safety threshold).
Figure 7: Camera pose sampling across four distinct floorplans. Green markers denote the final selected camera poses: red markers indicate poses discarded for being too close to the nearest mesh surface. Red outlines highlight ceiling-wal wwll intersection regions, while white outlines indi- cate floor-wall intersections.
C HIERARCHICAL INSTRUCTION GENERATION SCHEME
Grounded 3DGS reconstructions and automatically generated 2D semantic top-down maps, we -orientee design benchmarking oriented hierarchical instruction generation scheme to close the gap left by prior VLN benchmarks that largely focus on www-semantic-granllattt directives (e.g., "go from A to B atomic action sequences).
C.1 HIGH-LEVEL INSTRUCTIONS: TASK-SEMANTI ORIENTED
The most representative subset of High-Level Instructions is Single-Goal Semantic Instructions, which enriches basic "From A to B" navigation trajectories with semantic meaning. This subset addresses the limitation of traditional VLN benchmarks by linking navigation goals to human daily scenarios, object properties, spatial relationships. Detailed categories and examples are provided below:
(1) Add Object This category supplements logical causal relationship between the start point and destination by introducing contextually relevant objects, making the navigation trajectory conform to human daily behavior. Without such causality, directive like "from the sofa to the bookshelf* lacks practical meaning; adding a causal object (e.g.. "books") transforms it into a goal-driven task.
Casel: "Please move the book from the coffee table to the bookshelf in the study."
Case2: "Please move the teacup from the coffee table to the bookshelf in the study."
(2) Scenario Driven This category embeds specific human-centric scenario motive, framing the destination as a reasonable location to fulfill practical need. The instruction directly reflects human intentions (c.g.. thirst, hunger, rest), enabling the agent associate navigation with task utility.
Casel: "I'm thirsty, please bring me drink from the fridge."
Case2: "I want to rest, please take me to the sofa in the living room."
(3) Relative Relationship This category defines the target using relative spatial terms to distin- guish similar or adjacent objects an essential capability for navigating cluttered environments (e.g., multiple chairs, tables). Common spatial terms include "next to," "behind," "the one on the left," "across from," and "in front of."
Casel: "Move to the chair next to that table."
Case2: "Walk to the cabinet across from the fridge in the kitchen."
(4) Attribute-Based This category describes the target using perceivable, unique attributes to guide the agent in identifying specific object among similar candidates. Attributes include color (e.g.. "red"), state (c.g., "open," "opee,"""")) content (c.g., "empty." "full"), size (c.g., "large"), or decora- tion (e.g., "with flower pattern")).
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Casel: "Find empty table in the dining hall."
Case2: "Turn off the lit table lamp in the bedroom."
(5) Area-Based This category directs the agent to a general functional area rather than a specific object, focusing on spatial zones with practical purposes (e.g., cooking, resting, working). This is particularly useful for scenarios where the exact target object is unspecified but the functional context is clear.
Casel: "Walk from here to the kitchen area."
Case2: "Navigate to the lounge area in the living room."
C.2 LOW-LEVEL INSTRUCTIONS: BASIC NAVIGATION & ACTION ORIENTED
Complementing the task-semantic focus of High-Level Instructions, Low-Level Instructions priori- tize fundamental kinematic control and goal-directed point-to-point navigation without embedding complex contextual or functional semantics. These instructions serve two core purposes our VLN framework: (1) evaluating agent's basic motion execution capability (e.g., precise rota- tion, step control) and (2) providing a foundational navigation substrate for higher-level semantic tasks-acting as the "execution layer" that translates abstract High-Level goals into concrete move- ments. Unlike High-Level Instructions that answer "why to navigate," Low-Level Instructions focus solely on "how to move" or "where to go (without context)."
Below are the two primary categories of Low-Level Instructions, each tailored to assess distinct aspects of agent's low-level navigation competence:
C.2.1 1. BASE- ACTION: FUNDAMENTAL CONTROL BEHAVIORS
This category consists of goal-free primitive motions that test an agent's ability execute basic locomotor or rotational commands with precision. These actions lack any spatial target (e.g.. no specific object or area to reach) and instead focus on refining motion accuracy- critical prereq- uisite for smooth, collision-free navigation in continuous environments. Common Base-Actions include step- based forward/backward movement and Exed-angle rotation.
Moove forward two steps." Turr 90 degrees to the right in place."
Turrn 180 degrees to the left in place."
Movee backward one step."
C.2.2 SINGLE-GOAL: POINT-TO-POINS NAVIGATION
This category defines targeted point-to-point navigation tasks without additional semantic con- text-focusing solely guiding the agent from a start location predefined end location. The end location can be room, object, or functional zone, and the instruction is structured as a direct "go from X to Y" directive (or simplified to "go to Y" when the start location implicit). This category is further subdivided based on the type of start and end targets, covering common indoor navigation scenarios:
Room-to-Roome Navigate between two functional rooms. Casel:"Wall to the bedroom." Case2:"Go from the kitchen to the living room."
Room-to-Objest Navigate from Waal to the sofa in the living room." Case2:"Go from the study to the chair on the balcony."
to specific object within (or outside) the room.
Object-to-Object Navigate between two distinct objects. Case1:"War from the table to the door." Gase2:"Go from the fridge to the dining table."
Object-to-Roone Navigate from a specific object to a target room. Ca from the air conditioner to the kitchen." Case2:" from the desk the bedroom."
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Zone-to-Zone: Navigate between two functional sub-zones within larger space. Casel:"Walk from the center of the kitchen to the sink area." Case2:"Go from the TV area in the living room to the window."
These Single-Goal Low-Level Instructions are critical for benchmarking an agent's spatial ground- ing ability (e.g., recognizing "bedroom" "sofa" navigation targets) without the confounding effects of semantic context, making them ideal for initial model training or control-focused evalua- tions.
C.3 PROMPT FOR TRAJORIES To INSTRUCTIONS
With the rapid advancement MLLMs Yue et al. 2024a Li al. 2026; Zeng et al. [2024] Sepehrii
et al. 2025; Li et al. 2023), leveraging them for data generation has become widely adopted
paradigm Ye et al. 2024 Li al. 2025b). Below, we present the prompt employed for instruction generation.
Prompt for Instruction Generation
You specialized data annotator for roboties.
Your mission is act
human providing natural language instructions for a home or service robot. You will generate diverse set of buman-centric navigation instructions (of INSTRUCTION TYPE weighh-level-leeiveerr
based on a symbolic TEXT MAP, STARTING POINT,
and END POINT.
need to generate at least 2--4 instructions for each the seven INSTRUCTION TYPES defined below, ensuring variety and diversity.
(Input)
TEXT MAP: textual description an environment, including named areas, objects, and their unique IDs (c.g., Bar counter.0, chair.5). This map is the single source of truth.
2. STARTING POINT:
The starting point of the trajectory, represented by an object ID (e.g., c) Example: "starting point'': 'chair 5
END POINT: The endpoint of the trajectory, represented by an Object ID (e.g., sofa.0). Example: "andpoint"" "sofa.0'
<Task>
Generate multiple natural language instructions for a trajectory from the STARTING POINT to the END POINT (an optimal short path obtained via A*). Use the TEXT MAP understand the environment. Generate at least 2--4 instructions for each the INSCRUCTION TYPES below, ensuring diversity.
<Principles>
1. Don't Embellish Exaggerate: You path points or turns. Do not invent waypoints (e.g., \pass through d or directional conmands (e.g., \turn left") unless explicitly stated in the map.
know the intermediate
NEVER Use Internal IDs: Never include object IDs like chair.5. Instructions must be understandable to someone without the map.
3. Stay Grounded in the Map: De not invent objects, properties, spatial relationships not described reasonably inferable from the TEXT MAP.
4. Be Natural and Concise: Use everyday language. Keep instructions between 5200 words. Avoid robotic overly formal phrasing.
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5. Be Creative and Diverse: and perspective. Small wording changes should yield
Vary sentence structure, vocabulary,
meaningfully different instructions.
6. Avoid Repetition: Within each type,
instructions are semantically distinct Iet just synonyms minor rewordings.
7. Ensure Executability: Every instruction must be actionable using only the provided map.
8. Strictly Adhere to Types: Rach instruction must clearly match its assi gned type definition.
<nstzuuction.Tppss
1. Addoobject
Description: Adds reasonable causality object (e.g., an object to carry) to justify the movement. Examples:
2. upleaase move the book from the coffee table to the bookshel in the study.'
3. PPleas move the teacup from the coffee table to the bookshelf in the study."
4. Scenario Driven
Description Embeds the instruction human-centered scenaric or goal. Exampie: "I'm thirsty, please bring me drink from the friage."
5. Relative Relative.Rllaiionae Relationship
Description: Uses relative spatial terms (e.g., 'next to'', "behind'' "the one the left'') to identify the target. Example: 'Movve the chair next to that table.''
6. Attribute-based
Description: Describes the target using perceivable attributes window). Example: "Find an empty table in the dining hall."'
(e.g., empty, with vase,
7. Area-based
Description: Directs the rebot functional rather than specific object.
Exampie: "Walk from here to the kitchen arca.''
<Output Format>
For each instruction type, generate 2--4 diverse instructions. Output as a JSON array:
"Instruction_type"": ""ad__bbjctt',
'start'' _nd__ojeectiidd"" 'end''; [[providee_en__oojjeettt
comneratdd_insruuttion " "[jastructioo_teext""""
"'isttruction_type'': 'Area-based'',
"trafectory_id'' "'Ipoviied_idd""
'start'::
lendr' dearride___e__oeeee_____"""
"generated_instruttion" "[instructioo_texxt"""
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D COMPARISON OF OUR DATA WITH MATTERPORT3D
In this section, we compare our 3DGS-Mesh Hybrid Representation with traditional Matterport3D data. As shown in Fig. 8 MatterportäD's 's mesh derived from scanning, exhibits clear boundary ambiguity and object interpenetration, whereas our data uses collision bodies from the original mesh via convex decomposition, representing the ground truth. Rendered RGB images also show that our data is more photorealistic.
Matterport 3D Estimated Mesh
Our Ground Truth Mesh
Matterport 3D Rendering Scene
Ours high-qualine Rendering Scene
Figure 8: Comparison of Our data with Matterport3D.
E
MORE VISUALIZATION OF INTERIORGS
This section presents additional InteriorGS scenes. As shown in Fig, 9 these scenes highly detailed and photorealistic. demonstrating the high quality our indoor data. We anticipate that InteriorGS will become foundation for future embodied learning research.
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Distribution of Non-Home
Distribution of Assets
PIAST SAN
Agsnoy
origante with
Shand
Pusto
Services
Dific
oe
Soudio
took
Hall
C
Saaun intertainment
Becarr Vall
Rotall
Decorations
thou
emmment
Figure 10: Distribution of non-home scenes of Figure 11: Distribution of assets of InteriorGS. InteriorGS.
x
Figure 9: More Visualization of InteriorGS.
F DISTRIBUTION OF DATA FROM OUR INTERIORGS
In this section, further detail InteriorGS's s data distribution. Fig. 10 presents the distribution of 244 non-home scenes, categorized by function into Services, Office, Retail, Entertainment, and Fitness; Fitness has the fewest scenes, while the others are similarly distributed. Fig. IT shows the asset distribution, including Furniture, Lighting, Food & Drinks, Daily Items, Decorations, and Others; books within Others are the most numerous assets.
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Reviews:
[{"review_id": "uaImfTF8ce", "reviewer": "Reviewer_hytT", "summary": "This paper introduces **SAGE-3D**, a framework that enhances 3D Gaussian Splatting (3DGS) with object-level semantics and physical interfaces to create an executable environment for embodied navigation. The paper presents **InteriorGS**, a dataset of 1K annotated 3DGS indoor scenes, and **SAGE-Bench**, the first 3DGS-based Vision-Language Navigation benchmark. Based on the semantically and physically aligned 3DGS, the framework enables photorealistic rendering, semantic labeling, and physical collision modeling. Experiments show its effectiveness in improving model generalization and advancing embodied AI research.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "### **Strengths**\n1. The paper is well-organized and easy to understand, with clear explanations and intuitive figures illustrating the methodology.  \n2. The idea makes sense. 3DGS with object-level semantics and physical validity enables photorealistic rendering, semantic instance labeling, and physical interaction modeling. Based on this representation, a realistic and executable simulation environment can be created for embodied AI research, which is highly important. \n3. Experiments demonstrate that the dataset improves the generalization of current VLN models and, as a benchmark, can support future research in embodied navigation models.", "weaknesses": "### **Weaknesses and Questions**\n1. Creating the dataset appears to rely on detailed mesh scenes and extensive manual annotations, which may incur high costs and limit further scalability. It would be worth exploring more cost-efficient approaches for dataset construction, such as leveraging current vision-based semantic/geometry foundation models for scene reconstruction and semantic annotation.  \n2. The dataset seems to consist solely of static scenes. Introducing dynamic objects into the scenes could enhance its applicability and better simulate real-world scenarios for embodied AI research.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "a7c62xrJUp", "reviewer": "Reviewer_Wreg", "summary": "This paper introduces SAGE-3D, a novel paradigm that extends 3D Gaussian Splatting (3DGS) from a purely photorealistic rendering technique into a semantically and physically executable environment for embodied navigation. The authors propose two key modules—Object-Level Semantic Grounding, which augments 3DGS with dense, object-centric annotations, and Physics-Aware Execution Jointing, which integrates collision bodies to enable realistic physics simulation. Building on these components, the work releases InteriorGS, a large-scale dataset of 1,000 richly annotated indoor 3DGS scenes, and SAGE-Bench, the first 3DGS-based benchmark for vision-language navigation (VLN) with 2M instruction–trajectory pairs and new metrics for continuous navigation evaluation (Continuous Success Ratio, Integrated Collision Penalty, Path Smoothness). Experiments demonstrate that 3DGS environments render faster yet challenge model convergence, and that training on SAGE-Bench significantly improves generalization to unseen VLN settings. Overall, this work contributes an integrated data–simulation–benchmark framework that advances the use of 3DGS for physically grounded embodied AI.", "questions": "1. In the paper you mention that the high-level instructions are generated by feeding an MLLM (multilingual/multimodal large language model). Did you evaluate or validate the resulting instructions (e.g., accuracy, relevance, diversity)? Consider including a small user study or error analysis showing how instruction quality affects downstream navigation performance.\n2. For the component titled “2D Semantic Top-Down Map Generation”, will this design choice limit your dataset to planar/2.5D navigation tasks (e.g., ground-level walking) and restrict its applicability to fully 6DoF navigation scenarios (such as flying drones or free-roam agents)?\n3. You define the metric CSR (Continuous Success Ratio) as the proportion of time the agent stays within a permissible corridor around the reference path while satisfying task conditions. However, in many navigation tasks there may be multiple valid paths to the goal. In that case, is the “reference path” prior too restrictive — and is the metric truly fair? Could you discuss how you handle alternative optimal paths, whether you allow multiple corridors, or how you adjust for path diversity? Consider providing sensitivity analysis of CSR with respect to path choices.\n4. In Tables 2, 4, 5, and 6 many of the “best” results are not highlighted or visually distinguished. For improved readability and clearer takeaway for the reader, would you consider bolding or colouring the top‐performing numbers, adding a “best” row/column summary, or clarifying via annotation? This is a minor editorial issue, but it strongly affects accessibility of the results.\n5. You mention that models trained on your dataset are “harder to converge”, which you suggest might indicate higher task difficulty or richer learning. However — does harder to converge necessarily correlate with better training quality or better generalisation? It might also be a sign of optimization instability, noisy labels, or an ill-posed task. I suggest adding a deeper analysis.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "Originality: This work pioneers the first-of-its-kind benchmark built on the 3D Gaussian Splatting (3DGS) representation for vision-language navigation (VLN). By repurposing and re-contextualizing 3DGS—traditionally a rendering/novel-view synthesis technique—into the embodied navigation domain, the authors introduce a novel problem formulation and open a promising new research direction.\n\nQuality: The paper clearly presents its ideas with structured exposition and strong narrative flow. The methodology is articulated in accessible language, the dataset/benchmark design is sufficiently detailed, and the experiments convincingly demonstrate value. The clarity of presentation makes the core contributions easy to follow and the evaluation pipeline reproducible.\n\nClarity: The authors succeed in explaining the motivation, technical approach, dataset construction, task definitions and evaluation metrics in a straightforward manner. Complex concepts (e.g., converting 3DGS scenes into a navigation-capable environment) are broken down cleanly and the presentation avoids unnecessary jargon. As a result, the reader is able to readily understand both “what was done” and “why it matters”.\n\nSignificance: The proposed dataset and benchmark’s potential impact on the embodied AI community is substantial. By enabling faster rendered observations and high-quality 3DGS-based environments, this work could significantly accelerate research in navigation, simulation, and multimodal grounding. The resource thus has the capacity to become a standard tool or reference point for related tasks, facilitating broader progress in the field.", "weaknesses": "Limited reproducibility and generalisation of the paradigm: While the paper presents an interesting direction of adapting 3D Gaussian Splatting (3DGS) for embodied navigation, much of the work depends on manual annotation of objects and artist-created meshes/scene enrichment. The reliance on handcrafted assets makes it difficult for other researchers to easily replicate or scale the setup, and raises questions about how well the approach will generalise to entirely new scenes or domains without heavy annotation effort. The authors should consider describing a less labour-intensive pipeline for scene creation (e.g., automated annotation, mesh generation, domain adaptation) and discuss how one might extend their benchmarking assets beyond the current curated set.\n\nInsufficient discussion of experimental anomalies: In Table 2 the results show that several metrics for the “*-SAGE” variants (i.e., trained on the new 3DGS-based benchmark) are actually worse than baseline in some important metric(CR,ICP,PS)— yet the manuscript offers minimal explanation for these. Given these surprising outcomes, a deeper analysis is required. Without this discussion, the reader is left uncertain about the limitations and trade-offs of using the proposed dataset/benchmark.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vO23VM8QRE", "reviewer": "Reviewer_mZXf", "summary": "This paper proposes a new dataset and benchmark for visual-language navigation. The paper endows 3D Gaussian splatting with semantic meanings and physical properties to construct a comprehensive 3D environment for VLN. However, experiments do not show a clear improvement over the baselines.", "questions": "1. Why is the performance on SAGE-Bench worse than baselines in terms of CR, ICP and PS?\n2. How does the additional semantic and physical information of 3D GS benefit the performance? The paper whould conduct ablation study on that.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The motivation of integrating semantic and physical information into 3D Gaussian splatting is reasonable.\n2. The proposed dataset is large in scale and distinguish itself from others with diverse instructions and accurate geometry.\n3. The proposed evaluation metrics align well with the real application scenarios.", "weaknesses": "1. The performance on SAGE-Bench is worse than baselines in terms of CR, ICP and PS.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FEPuSvpeLj", "reviewer": "Reviewer_zosw", "summary": "This paper proposes a new paradigm named SAGE-3D (i.e., Semantically and Physically Aligned Gaussian Environments for 3D Navigation) that upgrades 3DGS into an executable, semantically and physically aligned environment. It contains two components, including Object-Centric Semantic Grounding and Physics-Aware Execution Jointing. It achieves faster scene rendering and better generalizability than scanned mesh data. Besides, this paper releases InteriorGS dataset containing 1000 manually object-annotated 3DGS scenes, and constructs SAGE-Bench including 2M new trajectory-instruction pairs and 554k detailed collision bodies. Extensive experiments demonstrate the effectiveness of the proposed paradigm.", "questions": "Please try to address the weaknesses.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "Strengths:\n1) The idea of SAGE-3D that upgrades 3DGS from a purely perceptual scene representation to an executable, semantically and physically aligned environment foundation is novel and valuable.\n2) The constructed InteriorGS containing 1000 manually object-annotated 3DGS scenes, is beneficial for the field developement. The SAGE-Bench, the first fully 3DGS-based VLN benchmark with 2M new trajectory-instruction pairs and 554K detailed collision bodies, is beneficial for the downstream applications.\n3) Extensive experiments are conducted on the proposed paradigm and validate the superiority of the newly introduced data.", "weaknesses": "Weaknesses:\n1) In the second paragraph of Introduction section, the authors claims 3DGS offers three key advantages, but only two items are listed as advantages.\n2) The organization of the scene data is based on GSplat, which may introduce more gaussian parameters than the mesh representation. Besides, the physics simulation introduces 3DGS-Mesh Hybrid representation, the collision bodies is computed by manually-created triangle mesh, does it introduce extra memory cost and rendering complexibility?\n3) The data generation of SAGE-Bench is developed based on the IneriorGS, but is similar with the construction of existing VLN datasets. What is the core contribution of SAGE-Bench? It is not clear.\n4) The definition of Nogoal-Nav is similar with visual exploration tasks, is there any difference? If there is no difference, there is no need to rename an existing task, visual exploration is more suitable.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)