Dynam3D: Dynamic Layered 3D Tokens Empower VLM for Vision-and-Language Navigation
Authors: Zihan Wang
Seungjun Lee
Gim Hee Lee
Venue: NeurIPS 2025 oral ()
Keywords: Vision-and-Language Navigation
3D Vision-Language Model
Primary area: applications
TL;DR: A multi-level patch-instance-zone 3D representation model with a hierarchical dynamic online update strategy for embodied navigation.

Abstract: Vision-and-Language Navigation (VLN) is a core task where embodied agents leverage their spatial mobility to navigate in 3D environments toward designated destinations based on natural language instructions. Recently, video-language large models (Video-VLMs) with strong generalization capabilities and rich commonsense knowledge have shown remarkable performance when applied to VLN tasks. However, these models still encounter the following challenges when applied to real-world 3D navigation: 1) Insufficient understanding of 3D geometry and spatial semantics; 2) Limited capacity for large-scale exploration and long-term environmental memory; 3) Poor adaptability to dynamic and changing environments.To address these limitations, we propose Dynam3D, a dynamic layered 3D representation model that leverages language-aligned, generalizable, and hierarchical 3D representations as visual input to train 3D-VLM in navigation action prediction. Given posed RGB-D images, our Dynam3D projects 2D CLIP features into 3D space and constructs multi-level 3D patch-instance-zone representations for 3D geometric and semantic understanding with a dynamic and layer-wise update strategy.  Our Dynam3D is capable of online encoding and localization of 3D instances, and dynamically updates them in changing environments to provide large-scale exploration and long-term memory capabilities for navigation. By leveraging large-scale 3D-language pretraining and task-specific adaptation, our Dynam3D sets new state-of-the-art performance on VLN benchmarks including R2R-CE, REVERIE-CE and NavRAG-CE under monocular settings. Furthermore, experiments for pre-exploration, lifelong memory, and real-world robot validate the effectiveness of practical deployment.

Dynam3D: Dynamic Layered 3D Tokens Empower VLM for Vision-and-Language Navigation
Zihan Wang Seungjun Lee Gim Hee Lee School of Computing, National University of Singapore zihan. wang@u. nus edu, gimhee. lee@nus edu. sg
Abstract
Aision-and-Languae Navigation (VLN) is a core task where embodied agents leverage their spatial mobility to navigate in 3D environments toward designated destinations based on natural language instructions. Recently, video-language large models (Video-VLMs) with strong generalization capabilities and rich com- monsense knowledge have shown remarkable performance when applied to VLN tasks. However, these models still encounter the following challenges when applied to real-world 3D navigation: 1) Insufficient understanding of 3D geometry and spatial semantics; Limited capacity for large-scale exploration and long-term environmental memory: 3) Poor adaptability to dynamic and changing environ- ments. To address these limitations, we propose Dynam3D, dynamic layered 3D representation model that leverages language-aligned generalizable, and hierar- chical 3D representations as visual input to train 3D-VLM in navigation action prediction. Given posed RGB-D images, our Dynam3D projects 2D CLIP features into 3D space and constructs multi-level 3D patch-instance zone representations for 3D geometric and semantic understanding with a dynamic and layer-wiss wise update strategy. Our Dynam3D capable of online encoding and localization of 3D instances, and dynamically updates them in changing environments to provide large-scale exploration and long-term memory capabilities for naviga- tion. By leveraging large-scale 3D-llaggage pretraining and -ase-spec adapta- tion, our Dynam3D sets new state-of-the-art performance on VLN benchmarks including R2R-CE, REVERIE-CE and NavRAG-CE under monocular settings. Furthermore, experiments for pre-exploration, lifelong memory, and real-world robot validate the effectiveness of practical deployment. The code is available at
Introduction
Vision-and sssion-and-lnguuee and-language navigation (VLN) tasks [14 require agents to integrate three core capabilities: 1) understanding natural language instructions, 2) exploring environments and localizing targets or destinations, and 3) planning and executing navigation actions. As illustrated in Figure Ia),, recent works 7 have predominantly focused on using video-based large models 10 develop monocular VLN systems. This is due to the practical constraint that most robots are equipped with monocular cameras instead of panoramic cameras. These models pre-trained large-scale internet data demonstrate strong language understanding and multimodal reasoning abilities, which enable effective instruction following and continuous prediction of navigation actions toward the destination.
Despite these recent advances, several limitations still remain: 1) Video-based models struggle to capture spatial geometry and semantics in large-scale 3D environments. Our experiments reveal that this significantly hinders the ability of these models to explore extensively and correct errors effectively. 2) These models lack mechanisms for structured scene memory. This prevents the use of
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Video - anguage large Model
Action
Instruction: go take the bread out of the microwave for me."
(a) Video-based VLM Navigation.
3D Language large Model
Action
geometry semantic understanding
exploration
Dynamic representation updates
(b) Our 3D Patch-instance-Zone VLM for Navigation.
Figure I: Different vision- vision-language large models for monocular VLN tasks. Compared to previous video-based representations (a), our Dynam3D (b) adopts dynamic hierarchical 3D representations offering advantages in spatial geometry and semantic understanding.
pre-exploration knowledge and limits the potential for lifelong learning. 3) Representations derived from historical frames inadequate for dynamically changing 3D scenes, where frequent object and human movements lead to performance drop.
We propose Dynam3D to alleviate the limitations mentioned above. As illustrated in Figure lb), our Dynam3D a 3DD-language model with dynamic layered representations for vision-and- languggg navigation. To encode 3D environments, we extract patch-level 2D features using CLIP [ and project them into 3D space via depth maps and camera poses. Our Dynam3D employs FastSAM to generate 2D instance masks, and aggregates patch features within each mask into instance-level representations. 3D instance merging discriminator aligns 2D instances with existing 3D instances based on geometry and semantics enable dynamic updates of 3D instance representations. Unlike previous online [33 or language-guide
12
[14] 3D segmentation methods that focus on mask accuracy, Dynam3D mainly aligns instance representations with the semantic space of CLIP through large-scale 3D-lanuuage pretraining to improve 3D representation quality and scene understanding.
Furthermore, our Dynam3D aggregates 3D instance features within spatial zones to facilitate un- derstanding large-scale environments.
a result, this enables high-level comprehension of layouts, e.g. bedrooms, kitchens, etc that instance-level features alone cannot capture. our Dynam3I updates the scene dynamically with this hicrarchical patch-instance-zont representation: outdated patch features are removed when new RGB-D observation arrives, and new features are projected and propagated across the representation layers (patch-instance-zom for change adaptation. These features enable our Dynam3D to maintain a lifelong and dynamic environmental memory that can significantly improve navigation performance in real-world deployments.
We further introduce generalizable feature field model [1] to render 3D patch features over an agent-centric panoramic scope for the enrichment of local geometric and semantic perception. These rendered 31) patch features combined with instance and zone representations serve as visual input to the 3D Vision- LaaguanL Model (VLM). Given language instructions and action history, the 3D-VLM directly predicts navigation actions, e.g., turn 0 degrees, move forward d cm, stop.
In summary, our main contributions include:
We propose Dynam3D, a multi-level patch- patch-instance-zone 3D representation model that performs online 3D instance and zone-level encoding and real-time hierarchical updates in dynamic environments.
We introduce a 3D Vision-Language Model that integrates 3D patch features from gener- alizable feature fields and 3D instance-zone features from our Dynam3D. This balances fine- fine-grained geometry and global spatial layout for navigation planning.
Our monocular VIN system achieves state-of-the-art performance on benchmarks includ- ing R2R-CE, REVERİE-CE, NavRAG-CE, The results also demonstrate our strong capabilities in pre-exploration, lifelong memory and real-world experiments.

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2
Related Work
Vision-and Vision-and-Language Navigation, Dision-and-Languaal Navigation (VLN) 1 16 19 re- quires the agent to understand complex natural language instructions and navigate to the described destination. In contrast to early works [16H18 20 21 which primarily concentrate on training and
evaluating models within discrete environment simulators 22
(i.e., move on the pre-defined navigation connectivity graph, equipped with panoramic RGB-D camera), recent researches have increasingly emphasized navigation in continuous environment simulators [23/27] and the real-world deployment of monocular VLN systems [S] 288 [5] 6 7 (29 30). For monocular VLN on continuous environment simulators, the agent equips only nerward-facing monocular RGB-D camera, and uses low-level actions to navigate. To leverage the language understanding and commonsense reasoning capabilities of large models, some recent works [3 29 have adapted 2D-VLMs to VLN tasks, leading to notable performance improvements. Extensions such as Va [5), Uni-Na Vid [6], and NaVILA 7 further exploit video-based large models to build nigh-perrfrrmnce monocular VLN systems with strong real-world applicability. However, video-based representations still have inherent limitations. For example, they struggle to capture fine-grained geometry semantics and comprehend large-scale spatial layouts, which in turn limits their capabilities in object localization and path planning. To the best of our knowledge, our Dynam3D is the first approach that effectively addresses the limitations inherent in previous video-based models by using 3D-VLM to perform monocular VLN tasks in unseen and dynamic environments.
10 recent works integrate 3D imputs, the point clouds [3% or multi-view images 41 to enable 3D scene reasoning for 3D-VLMs. These approaches differ primarily in scene representation: LL3DA [38 encodes full-scene point clouds directly; LEO [40] and Chat-Scene 39 decompose scene point clouds into object-level segments and encode corresponding features. 3D-LLM [2] and Scene- LLM 41 begin with multi-view images, apply 2D object segmentation, and aggregate CLIIP features
3D Tision-Languel Models. Inspired by the development of VLM
36
37
into pixel-aligned 3D points. VLaV3 [4] builds a pretrained 2D VLM (37 to embed 2D patches into 3D voxels via multi-view inputs and 3D positional embeddings. This enables fast adaptation to 3D tasks while maintaining strong 2D perception. However, current 3D-VLMs face fundamental challenges in large-scale unseen and dynamic tasks such as embodied navigation. Full- scene point cloud voxel-based representations are impractical for real-time reasoning in unseen environments. Existing models lack mechanisms for incremental updates, which make it difficult to revise or discard outdated scene information in dynamic contexts. Moreover, they struggle balance the computational trade-off between global spatial layout and fine-grained geometric semantics. In this context, we propose Dynam3D, 3D-VLM model that is better adapted for such dynamic embodied tasks.
3 Our Method
Overview. Figure shows the framework of our Dynam3D for vision-and- lan-uugg-- navigation. The framework takes the posed monocular RGB and depth images as input, and outputs atomic navigation actions such as turning, moving forward, stopping etc. Our Dynam3D maintains a set of patch feature points to encode the generalizable feature field [15) used to render the panoramic 3D patch tokens of the agent. Furthermore, our Dynam3D layer-by-layer encodes and updates 3D instance representations and large-scale cube zone representations for multi-level scene understanding and target localization (ef. Section 3.I. These multi-level 3D tokens, navigation instructions and history actions are then fed into a 3D-VLM VLM for next action prediction (cf. Section 3.2)
3.1 Dynamic Layered 3D Representation Model
We first design and pre-train multi-level 3D representation model to acquire language-aligned 3D representations encompassing both fine mme-grained details and global layouts.
Encoding the Patch Feature Points. To memorize the geometry and semantics of 3D environments, we follow HNR 455 and g3D-LF [15] in using CLIP- /33ir 336px [II] as the encoder for RGB images to extract 2D patch features {gc, E 76786 / T==1- Be, denotes the i-th patch feature of the 2D feature map extracted from 1-th frame observed by the agent and / 24 x 24. The patch features {g., V are then project to the corresponding 3D world coordinates {Pt, H using the depth map
3

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Render Panoramic Rays within Generalizable Feature Fields
Patch Feature Points In 3D Space
3D Project
3D wowe--ooss Saggaaaaaaaa Large Model
Depth
Panoramic
CLIP Visual Encoder
3D Patch Tokenn
Turn right dl degggnnn
Aggragite patch features within SD mask
image
FastSAM
patch features within 2D mask
3D Instance Masks
Instance Encoder
Marge instanous into wuiting
T
Tollens
Instance Encoder
Merging Discriminator
Zone Encoder
2D Instance Masks
20 Instance Features
Stop
3D Zone Tokens
Aggregate 3D instance within cube zone
Instruction: "Please to the kitchen ond take the bread out of the microwave for me."
00:0
Turn left 30 degree. Forward 50 cm, Turn left 45 degree. Forward cm.
History Actions
The architecture of our Dynam3D framework. Our Dynam3D takes posed monocular RGB and depth images as input and outputs atomic navigation actions. encodes and updates multi-level 3D representations for scene understanding and target localization. The 3D tokens, navigation instructions and history actions are then consolidated into the 3D-VLM for next action prediction.
Figure 2:
and camera parameters. For each feature Br.. the observed horizontal orientation Oz.i and the regional size St, are also calculated and stored to enhance the spatial representation. The set of feature points M can therefore be updated online as:
Mr = M,-1 U (l. Pe..,,,P,.s, Pt.,, Or, st.l
(I)
Updating the Patch Feature Points. As shown in Figure we employ the Frustum Culling strategy dynamically update the feature points set M by discarding outdated features and incorporating new ones, which differs from previous methods [46] 45 28 15]
simply add new feature points regardless of object motion or removal. Specifically, after obtaining the observed depth image D, R H the frustum culling strategy transforms the 3D world coordinate P.. M of each feature point into the pixel coordinate of the depth image using the camera pose [R, T and camera intrinsies K as
follows:
Jcc
Fe
PT
ye
R.P
+ +T,
K
ye
Ze_
FrustumCulling(Ae (Pw), if 0 < min(du,e 8, A), 0
< < H, and 0 O<U< W. (2)
dr,w denotes the depth value row and column w of the depth image D, E HxW, A feature point P., is removed from the feature points set M by the FrustumCulling() function when < 2e < min(d,,e - A). < u < and 0 < v < W, where is a noise threshold and the farthest culling distance. The frustum culling is first applied and followed by adding the new feature points when a RGB-D observation is obtained.
Dynamically Encoding 3D Instance Representations. Due to the overwhelming volume of 3D patch features, direct employment as visual input to 3D-VLM is computationally and economically impractical. In contrast to voxel-level pooling approaches, e.g. LLaVA-3D 44 our Dynam3D
encodes features the 3D instance level since target localization in navigation instructions is mostly described in terms of object instances. As illustrated in Figure 2 FastSAM [2] rapidly segments

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for rum nomowe//noeed object, frsstsm outiated patch features
on now
Bumose features from obnerved surthon
FasISAM
caobi callene
Tergungs of aligrment kos
Nayan Bacerearyy
add patch
2D Vnstanoss
Aesrage Pooiin
dicillation loes
Zoneleadi
CUP
anderr
Zone festure
Figure 3: Left: Illustration of the feature points update and frustum culling strategy. Right: The supervision of feature distillation and 3D-Janguage contrastive learning for our Dynam3D model.
the observed RGB image into of 2D instance masks. Within each mask, transformer- based instance encoder aggregates the corresponding patch features fg., }M with positional embeddings [Pm) pmmmm Jom into a compact instance-level representation O using learnable token q query:
= MLP PPm. Average(
Pm (Pm (mmm))) 8m, c0s( (in(m))) )
O = InstanceEncoderr(g {g... + (m.m)
(3)
In contrast to simple 2D instance representations, 3D instances require both multi-view and geometric consistency, enabling the agent identify the same instance across different views. To this end, we train a Merging Discriminator to integrate 2D instance representations into consistent 3D instances, as shown in Figure? 2 Initially, each 2D instance is treated as new 3D instance. At each subsequent step, for every new 2D instance, the Top-K K nearest existing 3D instances are retrieved. The Merging Discriminator evaluates each 2D-3D instance candidate pair using semantic and geometric encodings to determine correspondence. If no match is found among the Top-K candidates, new 3D instance created. Otherwise, the 2D instance is merged with the most similar 3D instance by concatenating their patch features and updating the 3D instance representation through the instance encoder. The 3D representation is updated with the remaining relevant patches when the outdated patches are removed via Frustum Culling. We discard the 3D instance in the case where all patches removed.
We train the Merging Discriminator using over 5K rooms with 3D instance segmentation data:
ScanNet (47), HM3D [48). Matterport3D (22) and 3RScan [49], where the annotation of instances of point clouds are processed for each point with world coordinate and instance ID. Ground truth instance IDs are assigned to patches by searching the nearest matching instance point from annotated instance point clouds. For each 2D or 3D instance, the majority ID of their patches determines the ground truth instance ID). The Merging Discriminator is trained with binary classification loss, where the label is positive (G = if the 2D and 3D instances share the same ground truth instance ID, or negative (g = 0) otherwise:
Lxngm
(4)
The function MerriingDisrrmmnnator is an MLP network which takes as imput the 2D instance fea- tures O?D, 3D instance features O and their Euclidean distance Dy k. and outputs 2-dimensional logit vector. After extensive pre-training, the function MesrinnDissriminaaorr efficiently inte- grates 2D instances into existing 3D instances to maintain mult-view and geometrically consistent 3D representations that can be updated.
Feature Distillation and Language Alignment for 3D Instances. To align 3D instances with language semantics, we leverage contrastive learning on large-scale 3DDllnguaage pairs from Scen- eVerse [50] and g3D-LF [15). Given a 3D instance feature O and its corresponding annotated language description feature T, extracted from CLIP text encoder, treat T, the positive sample
5

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and descriptions of other instances serve as negatives:
Linatones_
text
Crossaatooy((CosSnOOO, T)).)//...).
(5)
However, the generalization ability is limited by the scale of 3D-language data remains substantially smaller than that of image-language datasets: millions vs. billions [[]]. We thus further enhance
generalization by distilling visual knowledge from CLIP I into our Dynam3D model:
Sinstance_distillttii
CrossEntropy (Cassss(ssassssaa0a)) 091
(6)
To obtain the ground-truth instance feature Oft for distillation, we apply FastSAM generate 2D instance masks and adopt the Masked Average Pooling (MAP) strategy from Feature Splatting [5I) to average pool patch-level features within each instance mask and obtain O However, we observe that the instance-level features extracted in this strategy are interfered by noise from the overall image background. The ground-trutt instance features of the same 3D instance obtained from different views exhibit a sigmificant gap, which greatly affects the effectiveness of distillation since one of our goals is to achieve multi-view consistency in the representation of 3D instances. Consequently, we propose a strategy of Subspace Contrastive Learning:
Lunhapace ditiillttiitiiiiiinnn.
CrossEntropy ((Cosppyy(CCS(((( (O; - V3). - V3) 1/)
(7)
where V/ is computed by average pooling all patch features within the given 2D view to yield the local semantic center of this view, i.e. semantic subspace. In Equation instance features are optimized by maximizing cosine similarity with respect to the origin of the CLIP semantic space as the anchor. As a result, positive samples are pulled closer and negative samples are pushed farther apart. However, ground truth bias of different views can impede this contrastive process. In Equation 7 we replace the origin anchor with semantic center V of the view to mitigate the bias effect, impose a stronger optimization constraint and promote sparser feature space with improved representational capacity.
Feature Distillation and Language Alignment for 3D Zones. As shown in Figure and Figure 3 we introduce the zone-level representations 2 further capture coarse- grrriegg spatial layout context. Specifically, our Dynam3l) partitions the 3D world coordinate space into uniform cubic zones (each spanning several cubic meters) and employs zone encoder aggregate the instance-level features O within each zone to obtain Z. The encoding process is similar to Equation 3, For feature distillation at the zone level, our Dynam3D adopts relatively simple strategy: it uses zone encoder to aggregate 3D instances that belong to the same 2D view, and then aligns the aggregated zone representation Z with the CLIP feature of the entire 2D view. Although the aggregated instances do not strictly come from the same cube zone, this approach ensures the quality of the distilled ground- -ruth features. For zone-level language alignment, we follow g3D-LF [15 to use Fine-grained Contrastive Learning for long-text contrastive supervision. Specifically, we compute an affinity matrix between the instance representations within a zone and the long-text representations to measure similarity, and then perform contrastive learning across different zones and texts.
3.2 3D Vision-Language Model for Navigation
As illustrated in Figure 2 Dynam3D constructs hierarchical 3D representations, spanning from fine-grained object instances to large-scale environmental zones. Leveraging these multi-level 3D representations perceptual inputs, we introduce dedicated 3D Vision-Language Model (3D-VLM) tailored for VI.N tasks.
Encoding Panoramic 3D Patch Tokens via Generalizable Feature Fields. To effectively capture fine-grained geometric and semantic information within the surrounding panorama of the agent, we build upon the approach of g3D-LF [15] and adopt generalizable feature field model to predict agent-centric 3D patch tokens. Specifically, we uniformly sample 12 rays covering a 90 vertical and 360 horizontal field-of-view around the agent, rendering both the 3D patch features and their corresponding depth estimates. These features with positional embeddings provide rich and spatially grounded representations of the scene geometry and semanties from the egocentric viewpoint.
Multimodal Reasoning and Action Prediction. To balance multimodal reasoning capabilities with computational efficiency, the 3.8 aillion-paramee
[52 53 is integrated into

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the proposed 3D-V -LM framework. Since the 3D tokens seaich-instanne-aee zoznn) are aligned with the semantic space of CLIP- VIL---VIT-//4@"" 336px [T], the strong multimodal understanding and reasoning abilities of this 2D-VLM can be effectively transferred to the 3D domain.
As shown in Figure 2 the input and output format of our 3D-VLM is:
Input: < user > (patch_tokens) tokens) {instance stcencceeotkesseeee ) {
( tokenss
< end assistant
Output: Next action: 1) Turn left degree. 2) Turn right degree. 3) Forward d cm. 4) Stop.
<user> is special token in LLaVA 36 used to indicate that the following tokens are context. <end> marks the end of sequence. <asvistant> indicates that the following tokens are the response the model. To encode the relative positional relationship between 3D tokens and the agent, the relative coordinates [Te Ves zel. i.e. camera coordinates of each 3D token to the agent are calculated along with the relative distance D. and the relative horizontal angle 0.. [Tes ye: zeo De, cos(Oe), stn( of each token are then fed into a MLP network to generate the corresponding positional embeddings.
The 3D patch tokens {patch_tokens) rendered from the generalizable feature field are organized in row-major order of 12x48 tokens, starting from the rays directly behind the agent and proceeding clockwise. This strategy is similar to that used in the pre-trained LLLVA--hi----a when handling a single-view image. The instance tokens [imstance_loeens] and zone tokens zone__le [onke_s are sorted by their Euclidean distance to the agent from nearest to farthest. As shown Figure 2 3D-VILM outputs atomic actions with turning angles or movement distances. The history actions (history acciio_tooeen } store the four most recent action texts, padding with the special token < none > if fewer than four are available.
model [52 53
4 Experiments
4.1
Comparison with SOTA Methods
As shown in Tables D and 2 evaluate the navigation performance of our Dynam3D across three
distinct continuous-environneer VLN benchmarks. Specifically, the R2R-CE dataset (Tables D provides step-by-step and following instructions. Compared to prior state-of-the-art methods, e.g., g3D-LFF and Vni, our Dynam3D achieves an improvement of nearly 5% in navigation success rate (SR). Furthermore, despite the utilization of a large model, our Dynam3D maintains smaller parameter footprint (3.8B vs. 7B) relative to the video-based Uni-NaVid. This highlights the superior efficiency of model.
To ensure a fair comparison on the more challenging and realistic benchmarks such as REVERIE-CE which use coarse- coarse-grained and high-level destination description, and NavRAG-CE which requires understanding complex user demands, we retrain NaVid and g3D-LF on our training dataset and evaluate these two benchmarks (Table 2). Our Dynam3D still demonstrates substantial improve- ments, outperforming VaVid by over 13% in Success Rate (SR) on REVERIE-CE and by over 5% on NavRAG-CE. The detailed experimental setup can be found in the supplementary materials.
Table 1: Evaluation of VLN on R2R-CE with monocular setting. denotes zero-shot method.
R2R-CE
R2R-CE Test
Methods
LLM
Scene Representation
NEJ
OSRT
SRT
SPI.
NE.
OSRT SRT SPL.
CM [54)
Semantic Map
7.02
41.5
34.3
27.6
7.7
39
31
24
WS-MGMap
53
Multi-Granularity Semantic Map
6.28
47.6
38.9
34.3
7.11
45
35
28
InstructNav"
56
Semantic Value Map
6.89
31
24
AO AO-Planner"
57
Visual Affordance Prompts
6.95
38.3
25.5
16.6
NaVid [5]
Video Frames
5.47
49.1
37.4
35.9
VLN-3DFF 28
Feature Fields
55.8
44.9
30.4
6.24
54.4
43.7
28.9
g3D-LF [55
Feature Fields
5.70
59.5
47.2
34.6
6.00
57.5
46.3
32.2
Uni-NaVid 6
Multi Auati-Gramullrrtyy Vidco Frames
5.58
53.3
47.0
42.7
Dynam3D (Ours)
3D Patch Batch-Instance-Zone Tokens
5.34
62.1
52.9
45.7
5.53
60.4
51.4
44.8
4.2 Experiments on Pre-exploration and Lifelong Memory
As shown Table we additionally evaluate the performance under the Pre-exploration and Lifelong Memory settings to further demonstrate the advantages of our Dynam3D. The pre-explored panoramic
7

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Table 2: Evaluation of VLN on REVERIE-CE and NavRAG-CE with monocular setting. * denotes
zero-shot method.
REVERIE-CE Val
NavRAG-CA CE Val
Methods
LLM
Scene Representation
OSR* SRT SPL
NEI OSR
SRT
SPL1
InstructNav
[6
Semantic Value Map
7.44
31.5
25.2 19.1
9.83
24.1
17.4
10.9
NaVid S
Video Frames
6.74
36.3
26.6
20.8
9.35
29.6
19.4
13.9
g3D-LF
15
Feature Fields
6.50
41.6
34.4
23.8
8.85
31.8
21.4
13.5
DDnam3D (Ours)
3D Patch-Instance--Zone Tokens
6.22
48.9
40.1
28.5
8.12
38.4
24.7
18.8
images from the Pre-exploration setting collected at the navigable viewpoints annotated in the Matterport3D [22] dataset, which are then used to construct the Patch-Instance- Zone representations of the entire scene. For the Lifelong Memory setting, we group the evaluation episodes by scene with navigation samples from the same scene evaluated consecutively within a group. For each scene, previously stored 3D representations can be leveraged in subsequent episodes simulate gradual familiarization of the agent with the environment during task execution.
Table 3 shows that the Pre-exploration strategy enables our Dynam3D to achieve over 5% improve- ment in Success Rate (SR) on R2R-CE and an 8% improvement on REVERIE CE. Under the Lifelong Memory setting, our Dynam3D also achieves performance gains, with 2.7% SR improvement on R2R-CE and a 4.9% SR improvement on REVERIE-CE CEE Compared to VaVid [5] which uses a video-based large model, our Dynam3D employing both the Pre-exploration and Lifelong Memory achieves over a 20% increase in navigation success rate (SR).
Table 3: Evaluation of VLN for Pre-exploration and Lifelong Memory. Pre-exploration allows agents to scan and encode environmental representations before evaluation, while Lifelong Memory enables agents retain the environmental representations of previous episodes for subsequent episodes.
R2R-CE Val
REVERIE-CE CE Val
Methods
Pre-exploration
Lifelong Memory
NE.
OSR1 SR1 SPLT
NE.
OSR
SRT SPL
Vadid 5
x
5.47
49.1
37.4
35.9
6.74
36.3
26.6 20.8
g3D-LF 15
5.70
59.5
47.2
34.6
6.50
41.6
34.4
23.8
g3D-LF 15
5.46
62.5
51.8
39.9
6.44
43.3
37.1
25.9
Dynam3D (Ours)
X
X
5.34
62.1
52.9
45.7
6.22
48.9
40.1
28.5
Dynam3D (Ours)
5.04
66.2
57.1
52.7
6.09
56.8
48.1
37.3
Dynam3D (Ours)
5.21
64.4
55.6
48.1
6.31
52.8
45.0
32.7
Dynam3D (Ours)
5.11
67.2
58.4
50.4
6.02
56.4
49.5
38.1
4.3
Experiments on Real World and Dynamic Environment
As shown in Tables 4 5 and Figure we evaluate our Dynam3D on both real-world static and dynamic environments using the Hello Robot Stretch 3. Each setting includes 20 test cases, and navigation is deemed successful if the robot stops within meter of the target. In the static environment (Table Dynam3D achieves a 20% higher success rate than baselines, reaching 70% after pre-exploration. In the dynamic setting (Figure 4 and Table 5). the target is manually moved to another location once the rohot reach within two meters of the original target. our Dynam3D consistently outperforms all baselines, demonstrating strong robustness to environmental changes. The detailed experimental setup can be found in the supplementary materials.
Table 4: Real-world navigation experiments in static environments.
Table 5: Real-world navigation experiments
in dynamic environments.
Methods
NEJ OSRT
SR1
Methods
NEJ OSR1 SR
NaVid
2.2
45
35
NaVid
3.6
45
20
g3D-LF
3.1
40
30
g3D-LF
4.6
35
10
Dynam3D
1.4
65
55
Dynam3D
1.9
Pre-exxloration
0.8
75
70
+ Pre- Pre-exploration
1.4
75
45
4.4 Analysis for Dynamic Representation and Lifelong Memory
In our Dynam3D, the agent discards outdated representations within its current field-of-view and adds or updates new ones to ensure real-time understanding dynamic environments with each new observation. However, this does not conflict with its advantage of maintaining lifelong memory.
8

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bring bowl WWldd with apples from the choi."
Dynamic environment: During the robot's navigation white boul manually moved to another
Figure 4: demonstration of navigation in dynamic real-world environment.
Features corresponding to regions outside the robot's current view still preserved and not updated, and serve as long-term memory to support navigation. This due to the limited field of view of the monocular camera, where the majority of the scene is preserved since it lies outside the current view of the robot. These features are retained until new observations covering those regions are available update them. This highlights the ability of our Dynam3D balance real-time adaptability in dynamic environments with the effective use of long-term memory.
4.5
Computational Cost and Real- Reae-Time Analysis
We evaluate computational cost on the R2R-CE dataset using single NVIDIA RTX 4090 GPU. During training, each navigation step takes 455ms (~0.46 seconds) on average: 83ms for 3D representation updates, 315ms for large language model, and 57ms for other operations. During inference, the average step time increases 649ms (~0.65 seconds) with 83ms for 3D representation updates, 540ms for large language model inference, and 26ms for the remaining components. Most navigation episodes can be completed within 20 to 40 navigation steps, our navigation system supports real-time 3D representation updates and navigation action prediction for efficient training and inference.
4.6 Ablation Study
Table Ablation Study of Dynam3D R2R-CE and REVERIE-CE Val Unseen benchmarks. We
analyze the contribution of different components: input view type (currently observed Single View
patches vs. Rendered Panoramic patches), High-level 3D representations (Instance and Zone), and
the Subspace Align. loss used for supervising instance representations.
Subspace
K2R-CE Val Unseen
REVERTE-CE Val Unseen
Single Rendered
Instance
Align.
NEL
OSR
SRT
SPL.T
NEL
OSRT
SR
SPL.
43.9
32.7
21.1
8.76
27.4
11.4
1
1
6.14
51.7
43.1
33.9
7.85
35.9
28.4
21.8
X
x
X
x
5.63
51.1
45.7
40.2
7.89
34.8
25.7
17.8
54.7
47.9
43.3
X /
V
5.26
61.8
52.4
45.7
6.37
46.2
39.3
26.2
X
5.44
58.8
50.7
43.2
6.31
45.1
38.4
25.8
62.1
52.9
45.7
6.22
48.9
40.1
28.5
Contribution of 3D Representations. From the full model (last row), removing the rendered panoramic patch tokens (row 1), instance tokens (row 4), zone tokens (row 5), or both instance and zone tokens (row 3) leads to performance drops of varying degrees. The results show that the impact on performance follows the order: Patch > Instance Zone tokens.
Specifically, when both 3D instance and zone representations are removed and only 3D patch tokens from the feature fields [1] are used (row 3), there a substantial performance drop, particularly on REVERIE-CE, where SR decreases by nearly 15%. This highlights the critical role of the hierarchical instance-zone representation supporting effective navigation and large-scale exploration, as local patch-level features alone provide limited spatial coverage.

---

Importance of Subspace Alignment. Without Subspace Alignment supervision (row 6), the navigation performance sigmificantly decreases. This highlights the limitations of naive CLIP feature distillation for 3D instance supervision. Subspace Contrastive Learning effectively mitigates the instance feature bias from different views.
Value of Rendered Panoramic Views. As analyzed in VLN-3DFF (28] rendered panoramic patch tokens substantially mitigate the limited field of view caused by monocular cameras most robots. Comparing row 2 and last row in Table 6 replacing the rendered panoramic tokens with CLIP patch features extracted from the current monocular view results in about a 10% drop in navigation success rates on both R2R-CE and REVERIE
Table 7: Robustness study on the R2R-CE Val Unseen benchmark with the simulated SLAM noise and depth noise.
SLAM Noise
Depth Noise
NEC
OSR
SR1
SPL
X
X
5.34
62.1
52.9
45.7
X
5.46
61.3
52.1
45.0
/
5.39
61.9
52.3
45.2
/
5.47
60.7
50.9
44.1
Robustness to Noise. To further validate the model's navigation performance in noisy environments, we introduce different types of noise into the simulator and evaluate Dynam3D's performance. We simulate the potential noise from SLAM system in localization and pose estimation, well as the measurement errors inherent depth cameras.
To simulate SLAM noise, we introduce localization noise, uniformly sampled from -5 cm to +5 em, and orientation noise, uniformly sampled from -3 degrees +3 degrees, to the agent's position at each simulation step. To simulate depth camera noise, we add random noise to the depth maps acquired from the simulator. For depths within meters, noise sampled between em and cm; for depths from 2 to 3 meters, noise is sampled between -6 cm and +6 cm; for depths over 3 meters, noise is sampled between -10 cm and +10 cm.
As shown in Table 7 even with the simultaneous addition of simulated SLAM and depth noise, the navigation success rate (SR) only decreased by approximately 2% (comparing the last first row), demonstrating Dynam3D's robustness to noise. A significant reason is that our Dynam3D leverages 2D foundation models (e.g., CLIP MT, FastSAM (T2) extract visual semantic features, which are unaffected by SLAM and depth noise. Even with minor spatial position deviations due noise, the 3D-aware representations obtained by our model through the hicrarchical encoding of Batch-Instance-Zonn remain sufficient for perceiving spatial geometry and scene layout at the object and zone levels.
the
Conclusion
We introduce Dynam3D, dynamic hierarchical 3D representation framework for monocular vision- and-language navigation. By aligning patch-instance-zon features with language semantics and enabling real-time scene updates, our Dynam3D enhances spatial understanding, long-term memory, and adaptability in dynamic environments. Our model achieves state-of-the-art results on multiple VLN benchmarks and demonstrates strong generalization in real-world deployment. These results highlight the value of structured and dynamically updated 3D representations for embodied navigation.
I Limitations. Our Dynam3D predicts navigation actions without explicitly outputting the coordinate of target instance, limiting applicability to some tasks such as mobile manipulation. Moreover, lacks capabilities for question answering, dialogue, and task updates, showing potential directions for better navigation agents.
10

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Acknowledgement
This research work supported by the Agency for Science, Technology and Research (A*S)AR) un- der its MTC Programmatic Funds (Grant No. M23L7b0021), and the Tier 2 grant MOE-T2EP20124- 0015 from the Singapore Ministry of Education.
References
[1] Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf,, Ian Reid, Stephen Gould, and Anton Van Den Hengel. Vision-and -asio--an-languuaee navigation: Interpreting visually visually-grounded navigation instructions in real environments. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3674-3683 2018.
[2] Yuankai Qi, Qi Wu, Peter Anderson, Xin Wang. William Yang Wang, Chunhua Shen, and Anton van den Hengel. Reverie: Remote embodied visual referring expression in real indoor environments. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9982 9991, 2020.
[3] Jacob Krantz, Erik Wijmans, Arjun Majumdar, Dhruv Batra, and Stefan Lee. Beyond the nav- graph: Vision-and aane-aand-nggagg navigation in continuous environments. Computer Vision-ECCC 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXVIII 16, pages 104-120. Springer, 2020.
[4] Zihan Wang, Yaohui Zhu, Gim Hee Lee, and Yachun Fan. Navrag: Generating user de- mand instructions for embodied navigation through aariival-augmented 1lm. arXiv preprint arXiv:2502.11142. 2025.
[5] Jiazhao Zhang, Kunyu Wang, Rongtao Xu, Gengze Zhou, Yicong Hong, Xiaomeng Fang,
Qi Wu, Zhizheng Zhang, and He Wang. Navid: Video-based vim plans the next step for vision-and -nn--aggu navigation. In Proceedings of Robotics: Science and Systems (RSS), 2024.
[6] Jiazhao Zhang, Kunyu Wang, Shaoan Wang, Minghan Li, Haoran Liu, Songlin Wei, Zhongyuan Wang, Zhizheng Zhang, and He Wang. Uni-navid: A video-based vision-language-actinn model for unifying embodied navigation tasks. arXiv preprint arXiv:2412.06224, 2024.
[7] An-Chich Cheng, Yandong Ji, Zhaojing Yang. Xueyan Zou, Jan Kautz, Erdem Biyik, Hongxu Yin. Sifei Liu, and Xiaolong Wang. Navila: Legged robot vision-language-attion model for navigation. In RSS, 2025.
[8] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023.
[9] Lin, Hongxu Yin, Wei Ping, Pavlo Molchanov, Mohammad Shoeybi, and Song Han. Vila: On pre-training for visual language models. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 26689 66689-26699, 2024.
[10] Wei-Lin Chiang, Zhuohan Li, Ziqing Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. Vicuna: An open-source chatbot impressing gpt-4 with 90% chatgpt quality. See https://vicua. Imsys. org (accessed 14 April 2023), 2(3):6, 2023.
[11] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference machine learning,
pages 8748-8763 8763. PMLR, 2021.
[12] Xu Zhao, Wenchao Ding, Yongqi An, Yinglong Du, Tao Yu, Min Li, Ming Tang, and Jinqiao Wang. Fast segment anything. arXiv preprint arXiv: 12156, 2023.
[13] Xiuwei Xu, Huangxing Chen, Linqing Zhao, Ziwei Wang, Jie Zhou, and Jiwen Lu. Embodied- sam: Online segment any thing real time. In The Thirteenth International Conference on Learning Representations.

---

[14] Seungjun Lee, Yuyang Zhao, and Gim Hee Lee. Segment any 3d object with language. In The Thirteenth International Conference on Learning Representations.
[15] Zihan Wang and Gim Hee Lee. g3d-If:: Generalizable 3d-language feature fields for embodied tasks. arXiv preprint arXiv:2411.1700 2024.
[16] Yicong Hong. Qi Wu, Yuankai Qi, Cristian Kodriguez-Opaza and Stephen Gould, Vin bert: A recurrent vision-and- ann-language bert for navigation. In Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition, pages 1643-1653, 2021.
[17] Shizhe Chen, Poerre-Louis Guhur, Cordelia Schmid, and Ivan Laptev. History aware multimodal transformer for vision-and- languuge navigation. Advances neural information processing systems, 34:5834-5847, 2021.
[18] Shizhe Chen, Pierre-Louis Guhur, Makarand Tapaswi, Cordelia Schmid, and Ivan Laptev. Think global, act local: Dual-scale graph transformer for vision-and- iisionaadd-language navigation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 16537-16547, 2022.
[19] Liuyi Wang, Zongtao He, Ronghao Dang, Mengjiao Shen, Chengju Liu, and Qijun Chen. Vision- and-language navigation via causal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13139-13150, 13150, 2024.
[20] Yanyuan Qiao, Yuankai Qi, Yicong Hong, Zheng Yu, Peng Wang, and Qi Wu. Hop+: History- asiion-ad-language enhanced and order-aware pre-tree pre-training for vision-and language navigation. IEEE' Transactions on Pattern Analysis and Machine Intelligence, 45(7):8524 8537755477337,, 2023.
[21] Rui Liu, Xiaohan Wang, Wenguan Wang, and Yi Yang. Bird's-eye-view scene graph for vision- language navigation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10968-10980) 2023.
[22] Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niebner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. In International Conference on 3D Vision (3DV), 2017.
[23] Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, al. Habitat: A platform for embodied ai research. In Proceedings of the IEEF/CVF international conference on computer vision, pages 9339-9347, 2019.
[24] Yicong Hong, Zun Wang, Qi Wu, and Stephen Gould. Bridging the gap between learning in discrete and continuous environments for vision-and-languree navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
[25] Zihan Wang, Xiangyang Li, Jiahao Yang, Yeqi Liu, and Shuqiang Jiang. Gridmm: Grid memory map for vision-and aan-uagg navigation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15625-15636. 2023.
[26] Dong An, Hanqing Wang, Wenguan Wang, Zun Wang, Yan Huang, Keji He, and Liang Wang. Etpnav: Evolving topological planning for vision-language navigation in continuous environments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
[27] Dong An, Yuankai Qi. Yangguang Li, Yan Huang, Liang Wang, Tieniu Tan, and Jing Shao. Bevbert: Multimodal map pre-training for language-guidee navigation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2737-2748 2023.
[28] Zihan Wang, Xiangyang Li, Jiahao Yang, Yeqi Liu, and Shuqiang Jiang. Sim-to-real transfer via 3d feature fields for aision-ndd-lanuuan navigation. In 8th Annual Conference on Robot Learning, 2024.
[29] Yanyuan Qiao, Wenqi Lyu, Hui Wang, Zixu Wang, Zerui Li, Yuan Zhang, Mingkui Tan, and Qi Wu. Open-nav: Exploring zero-shot environment with open-source lims. Proceedings of the IEFE International Conference on Robotics and Automation (ICRA), 2025.
navigation in continuous
12

---

[30] Sixian Zhang, Xinhang Song, Xinyao Yu, Yubing Bai, Xinlong Guo, Weijie Li, and Shuqiang Jiang. Hoz++: Versatile hierarchical object-to- toze graph for object navigation. IEEE Transac- lions on Pattern Analysis and Machine Intelligence, 2025.
[31] Duo Zheng, Shijia Huang. Lin Zhao, Yiwu Zhong, and Liwei Wang. Towards learning generalist model for embodied navigation. In Proceedings of the IEEF/CVF Conference on Computer Vision and Puttern Recognition, pages 13624-13634, 2024.
[32] Gengze Zhou, Yicong Hong. Zun Wang, Xin Eric Wang, and Qi Wu. Navgpt-2: Un- leashing navigational reasoning capability for large vision-language models. arXiv preprint arXiv:2407. 12366, 2024.
[33] Jiaqi Chen, Bingqian Lin, Ran Xu, Zhenhua Chai, Xiaodan Liang, and Kwan-Yee K. Wong. Mapgpt: Map-guided prompting with adaptive path planning for In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, 2024.
navigation.
[34] Sixian Zhang, Xinyao Yu, Xinhang Song, Xiaohan Wang, and Shuqiang Jiang. Imagine before go: Self-supervised generative map for object goal navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16414-16425, 2024.
[35] Jiaqi Chen, Bingqian Lin, Xinmin Liu, Lin Ma, Xiaodan Liang, and Kwan- Yee K. Wong. Affordances-orientee planning using foundation models for continuous vision-language naviga- tion. In Proceedings of the AAAI Conference on Artificial Intelligence, 2025.
[36] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, Advances in neural information processing systems, 36:34892-34916, 2023.
[37] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22226-26336,, 2024.
[38] Sijin Chen, Xin Chen, Chi Zhang, Mingsheng Li, Gang Yu, Hao Fei, Hongyuan Zhu, Jiayuan Fan, and Tao Chen. LI3da: Visual interactive instruction tuning for omni-3d understanding reasoning and planning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26428 26438,643822 2024.
[39] Haifeng Huang, Yilun Chen, Zehan Wang, Rongjie Huang, Runsen Xu, Tai Wang, Luping Liu, Xize Cheng, Yang Zhao, Jiangmiao Pang, al. Chat-scene: Bridging 3d scene and large language models with object identifiers. arXiv preprint arXiv:2312.08166 2023.
[40] Jiangyong Huang, Silong Yong, Xiaojian Ma, Xiongkun Linghu, Puhao Li, Yan Wang, Qing Li, Song-Chun Zhu, Baoxiong Jia, and Siyuan Huang. An embodied generalist agent in 3d world. In Proceedings of the International Conference Machine Learning (ICML), 2024.
[41] Rao Fu, Jingyu Liu, Xilun Chen, Yixin Nie, and Wenhan Xiong. Scene-llm: Extending language model for 3d visual understanding and reasoning. arXiv preprint arXiv:2403. 1401, 2024.
[42] Yining Hong, Haoyu Zhen, Peihao Chen, Shuhong Zheng, Yilun Du, Zhenfang Chen, and Chuang Gan. 3d-lim: Injecting the 3d world into large language models. Advances in Neural Information Processing Systems, 36:20482-204499 2023.
[43] Duo Zheng, Shijia Huang, and Liwei Wang. Video-3d llm: Learning position-aware video representation for 3d scene understanding. arXiv preprint arXiv:2412.00499; 2024.
[44] Chenming Zhu, Tai Wang, Wenwei Zhang, Jiangmiao Pang. and Xihui Liu. Llava-3d: A simple yet effective pathway to empowering Imms with 3d-awareness. arXiv preprint 2rXiv:2409.18/22 2024.
[45] Zihan Wang, Xiangyang Li, Jiahao Yang, Yeqi Liu, Junjie Hu, Ming Jiang, and Shuqiang Jiang. Lookahead exploration with neural radiance representation for continuous vision-language navigation. In Proceedings of the IEFF/CVF Conference on Computer Vision and Pattern Recognition, pages 13753-13762, 2024.
13

---

[46] Ri-Zhao Qiu, Yafei Hu, Ge Yang, Yuchen Song, Yang Fu, Jianglong Ye, Jiteng Mu, Ruihan Yang, Nikolay Atanasov, Sebastian Scherer, et al. Learning generalizable feature fields for mobile manipulation. arXiv preprint arXiv:2403.07555 2024.
[47] Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießnnr. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEFE conference on computer vision and pattern recognition, pages 5828-5839 2017.
[48] Karmesh Yadav, Ram Ramrakhya, Santhosh Kumar Ramakrishnan, Theo Gervet, John Turner, Aaron Gokaslan, Noah Maestre, Angel Xuan Chang, Dhruv Batra, Manolis Savva, al. Habitat- matterport 3d semantics dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4927 4936, 2023.
[49] Johanna Wald, Armen Avetisyan, Nassir Navab, Federico Tombari, and Matthias Nießner. Rio: 3d object instance re-localization in changing indoor environments. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7658-7667, 2019.
[50] Baoxiong Jia, Yixin Chen, Huangyue Yu. Yan Wang. Xuesong Niu, Tengyu Liu, Qing Li, and Siyuan Huang. Sceneverse: Scaling 3d vision-language learning for grounded scene understanding. In European Conference on Computer Vision (ECCV), 2024.
[51] Ri-Zhao Qiu, Ge Yang, Weijia Zeng, and Xiaolong Wang. Feature splatting: Language-driven physics-based scene synthesis and editing. arXiv preprint arXiv:2404.01223, 2024.
[52] XTuner Contributors. Xtuner: A toolkit for efficiently fine-tuning llm. https: com/ Intern[M/xtuner 2023.
[53] Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, Ammar Ahmad Awan, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, et al. Phi-3 technical report: highly capable language model locally on your phone. arXiv preprint arXiv:2404. 14219, 2024.
[54] Georgios Georgakis, Karl Schmeckpeper, Karan Wanchoo. Soham Dan, Eleni Miltsakaki, Dan Roth, and Kostas Danillidis. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15460-154477 2022.
map learning for vision and language navigation. In
[55] Peihao Chen, Dongyu Ji, Kunyang Lin, Runhao Zeng. Thomas Li, Mingkui Tan, and Chuang Gan. Weakly-supervised multi-granularigg map learning for vision-and-Le lan-uag navigation. Advances in Neural Information Processing Systems, 35:38149-23 19938161, 2022.
[56] Yuxing Long, Wenzhe Cai, Hongcheng Wang, Guanqi Zhan, and Hao Dong, Instructnav: Zero-shot system for generic instruction navigation in unexplored environment. In 8th Annual Conference Robot Learning, 2024.
[57] Jiaqi Chen, Bingqian Lin, Xinmin Liu, Lin Ma, Xiaodan Liang, and Kwan-Yes K Wong. Affordances-oriented planning using foundation models for continuous vision-language nav- igation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 13668-23576, 2025.
[58] Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Yuri Feigin, Peter Fu, Thomas Gebauer, Daniel Kurz, Tal Dimry, Brandon Joffe, Arik Schwartz, et al. Arkitscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1).
[59] Jia Zheng, Junfei Zhang, Jing Li, Rui Tang, Shenghua Gao, and Zihan Zhou. Structured3d: A large photo-realistic dataset for structured 3d modeling. In Proceedings of The European Conference on Computer Vision (ECCV), 2020.
[60] Zun Wang, Jialu Li, Yicong Hong, Yi Wang, Qi Wu, Mohit Bansal, Stephen Gould, Hao Tan, and Yu Qiao. Scaling data generation in vision-and-languaas navigation. Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12009-12020, 2023.
14

---

[61] Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the fourteenth interna- lional conference on artificial intelligence and statistics, pages 627-635. JMLR Workshop and Conference Proceedings, 2011.
[62] Noam Shazeer and Mitchell Stern. Adafactor: Adaptive learning rates with sublinear memory cost. In International Conference on Machine Learning, pages 4596-4600 PMLR, 2018.
[63] Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. Training deep nets with sublinear memory cost. arXiv preprint arXiv: 1 1604.66144 2016.
[64] Peiqi Liu, Zhanqiu Guo, Mohit Warke, Soumith Chintala, Chris Paxton, Nur Muhammad Mahi Shafiullah, and Lerrel Pinto. Dynamem: Online dynamic spatio-semantic memory for open world mobile manipulation. In CoRL 2024 Workshop on Mastering Robot Manipulation in World of Abundant Data.
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A Supplementary Material
A.1 Datasets and Experimental Details
3D-Language Datasets and Training Details. To train the Dynam3D representation model, we follow Scene [50] and g3D-LF (T5] in collecting over 5K scenes with 2M language annotations from ScanNet 47.. HM3D [48], Matterport3D [22]. 3RScan 49), ARKitScenes [58), and Struc- tured3D [59] For each episode, we randomly sample sufficient posed RGB-D images from raw videos or the Habitat simulator [23] to construct and update the hierarchical patch-instance-zone representations. The updated representations after each observed frame is supervised using the losses defined in Section 3.1 We pre-train our Dynam3D representation model on the aforementioned dataset for 100K episodes (approximately 8 days) using four RTX 6000 Ada GPUs. The training is performed with a batch size of 4 and a learning rate of le-4.
Navigation Datasets and Training Details. To train our 3D-VLM with sufficient navigation data, we transfer datasets generated by Scale VLN [60] and NavRAG [4] from discrete environments to the continuous Habitat simulator [23]. After removing samples with impassable paths, obtain 4M+ instruction-traaectoryy pairs in continuous settings. For a comprehensive and fair evaluation, we evaluate our model on R2R-CE [3) REVERIE- and NavRAG-CE by transferring REVERIE 2 and NavRAG 4 datasets to continuous environments, To balance data quality and scale, we randomly sample model--ennerated data (L.aleVLNN, NavRAG; 4M+) and human-annotated data (R2R-CE, REVERIE-CE; 20K+) at 1:1 ratio during 3D-VLM training. Navigation training proceeds in two stages: 1) Imitation learning. The agent strictly follows ground-truth paths to enhance instruction following and multimodal alignment; 2) Exploration and correction. Following ETPNav (26), we adopt a waypoint predictor [24] to generate multiple candidate waypoints. We utilize the DAgger strategy 68 enhance error correction by deliberately introducing probabilistic deviations that mislead the agent towards incorrect waypoints. The agent is then guided back to the correct path, thereby strengthening its ability to recover from navigation errors. We pre-train the 3D-VLM model on the navigation datasets for 100K episodes (50K for stage one, 50K for stage two, approximately days) using two RTX 6000 Ada GPUs. The training is performed with batch size of 4 and learning rate of le-6. During training, all parameters of the 3.8B L.LaVAPPhi33-mini 52 53 are optimized, except the generalizable feature field model 15 and the pre-trained Dynam3D representation model. To mitigate memory consumption and enable efficient training of large models, we employ the Adafactor optimizer 662 conjunction with Gradient Checkpointing (63)
Details of Real-world Navigation. We employ the Hello Robot Stretch 3 for real-world naviga- tion experiments, leveraging its real-time localization and pose estimation capabilities. An Intel RealSense D435i RGB-D camera mounted on the robot's 's head to facilitate 3D scene representation construction and incremental updates. Our DynaMem (64), with extensions for obstacle avoidance and movement. The model is deployed on workstation equipped with an NVIDIA RTX 4090 GPU and 64GB of RAM, and communicates with the robot over a local area network established via a WiFi access point. The experimental environment consists of home-style setting constructed for robot evaluation, encompassing living room, kitchen, meeting room, and office. To ensure a fair comparison under the unseen setting, none of the objects or rooms within the environment are included in the training data.
experimental framework is adapted from
23


Reviews:
[{"review_id": "s1wR2vnDjL", "reviewer": "Reviewer_V8c4", "summary": "This paper presents Dynam3D, a dynamic layered 3D representation framework for vision-and-language navigation that addresses limitations of existing video-based approaches through three primary contributions. First, the authors develop a hierarchical patch-instance-zone representation model that projects 2D CLIP features into 3D space and maintains multi-view consistent 3D instances using a merging discriminator trained on large-scale 3D segmentation data. Second, they implement a dynamic update mechanism employing frustum culling to remove outdated features and real-time hierarchical updates to adapt to changing environments. Third, they pass in these representations into a 3D Vision-Language Model that combines panoramic 3D patch tokens.", "questions": "N/A - see weaknesses", "limitations": "N/A", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n* Technical innovation: Clearly identifies gaps in the current SOTA and addresses it with their method.\n* Results: Demonstrates clear strength in performance in both simulated and real world setting.\n\nWeaknesses:\n* It is unclear how this method scales to larger environments - especially outdoors. \n* By extension, it is unclear to me how this method would work if frames end up being drastically different from each other.", "was_revised": true, "final_justification": "All followup questions have been answered, nothing remains unresolved. I would like to keep my initial high score.", "rebuttal": "## **1. Handling Large-Scale Scenes, e.g., Outdoors**\nThanks for your valuable comments! Although currently primarily designed for indoor navigation, our Dynam3D model architecture is in fact also suitable for outdoor navigation.\n### **1) Representation and Computational Overhead**\nUnlike fine-grained voxel-based methods, our instance and zone-level tokens effectively handle large-scale scene representation without introducing an excessive number of tokens and huge computational overhead. The number of 3D patch tokens rendered via feature fields is fixed at 12x48. Furthermore, the KD-Tree feature retrieval and sparse sampling strategies employed in HNR and g3D-LF ensure highly efficient feature field rendering. Even in outdoor urban scenes, a single RTX 4090 GPU can render 12x48 panoramic patch tokens at over 15 FPS.\n### **2) Generalization and Training Data**\nAlthough the model architecture of our Dynam3D can be adapted to large scenes and outdoor navigation, its current generalization capabilities for outdoor navigation are limited because the training data of both the 3D representation and the 3D-VLM model originate from indoor environments. However, it’s possible to fine-tune and adapt the model using outdoor navigation datasets such as Touchdown, map2seq, and VLN-VIDEO.\n### **3) Outdoor Robot Deployment**\nFor indoor navigation, the Stretch 3 robot used in the Dynam3D experiments is equipped with a precise SLAM system, accurate camera pose estimation, and sufficient depth camera precision. However, the localization system and depth camera do not perform well in outdoor environments due to the narrow field-of-view of the LiDAR on the robot. Nonetheless, this issue can be easily mitigated by equipping the robot with a wide-angle 4D-LiDAR to increase the field-of-view. Our Dynam3D model supports sensory inputs from the wide-angle LIDAR with no need for drastic modifications.\n\n## **2. Handling Large Inter-Frame Changes**\nThe frustum culling of Dynam3D and its token addition, deletion, and update strategies are well-suited to handle significant changes between observation frames.\n### **1)  Large robot and camera movements leading to significant inter-frame changes.**\nIn such scenarios, the overlap between the current frame and historical frames is minimal.  Our Dynam3D handles this situation effectively by primarily adding new patch, instance, and zone tokens. For example, it directly encodes 2D instances as new 3D instances as described in lines 152–156 of the original paper. This eliminates the need for extensive 2D-to-3D instance merging.\n### **2) Rapid movement of people or objects within the scene causing huge inter-frame changes.**\nFrustum culling can effectively delete old features and update new ones when a moving object moves away from or traverses out of the camera field-of-view. However, frustum culling may fail to remove outdated features from earlier frames when objects move closer to the camera. This is due to occlusions caused by objects in the new frame. A potential future improvement is to address this issue using 3D object or point tracking methods."}, {"review_id": "JMiomvGEyQ", "reviewer": "Reviewer_QXMr", "summary": "This paper proposes Dynam3D, a novel framework for vision-language navigation (VLN) that introduces a dynamic hierarchical 3D scene representation. The representation integrates patch, instance, and zone tokens, addressing challenges in spatial understanding, long-term memory, and flexibility to dynamic environments. The authors combine this representation with a pretrained VLM LLaVA-Phi-3-mini, and demonstrate superior performance across VLN benchmarks (R2R-CE, REVERIE-CE, NavRAG-CE). Furthermore, Dynam3D exhibits significant results in real-world navigation tasks, including static and dynamic environments.", "questions": "- I think the delivered message in the experiments of pre-exploration and lifelong memory (Section 4.2) can be polished. There is no doubt that pre-exploration would improve the performance, and it cannot highlight the advantage of Dynam3D since the comparison with other methods without pre-exploration is unfair. Instead, I think the proper message here is that Dynam3D makes lifelong memory feasible and thereby achieves a similar effect to pre-exploration.\n- I would expect more details on the processing of VLN data, such as action merging, label smoothing or rebalancing. These details can be important to the final performance of VLN as demonstrated by prior works.\n- An interesting and important question is how well the merging discriminator can perform. Are the merging results accurate? Providing some simple evaluations about the merging accuracy would offer more insights.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "#### Strengths\n- The methodology is well-grounded and technically sound. The idea of dynamically updated patch-instance-zone 3D representations is both innovative and well-motivated. And Dynam3D incorporates thoughtful designs for dynamic environments, including Frustum Culling and merging discriminator.\n- The experimental results are significant, highlighting the strengths of Dynam3D in performing VLN tasks. In particular, Dynam3D shows strong performance in real-robot experiments.\n\n#### Weaknesses\n- I think the representation design is kind of overcomplicated. The pipeline for deriving various kinds of tokens involves complex procedures such as segmentation with FastSAM and rendering with g3D-LF. My concern persists despite the reported computational costs and real-time analyses. While it cannot be a major weakness to propose many dedicated and specific designs, it can make the method less appealing.\n- Given such dedicated designs, I would expect more ablation results to figure out the contributions of different parts to the performance. For example, the contributions of 3D instance tokens and panoramic 3D patch tokens are not clearly reported. On the other hand, the patch, instance, and zone tokens represent different granularities. So my concern is that would such a heterogeneity confuse the model learning (e.g., can model distinguish their token types)?", "was_revised": true, "final_justification": "Technically solid and insightful paper. I recommend accept.", "rebuttal": "Thank you very much for your insightful comments! In response, we include some previously omitted experiments and added new results. This provides a clearer presentation of the contributions of our method.\n\n| #   | Single View Patch | Rendered Panoramic Patch | Instance | Zone | R2R-CE Val  Uneen   | R2R-CE Val  Uneen   | R2R-CE Val  Uneen  | R2R-CE Val   Uneen  | REVERIE-CE Val  Uneen | REVERIE-CE Val  Uneen | REVERIE-CE Val  Uneen | REVERIE-CE Val  Uneen |\n| --- | ----------------- | ------------------------ | -------- | ---- |:--------------:|:-------------:|:------------:|:-------------:|:--------------:|:--------------:|:--------------:|:--------------:|\n|     |                   |                          |          |      | NE$\\downarrow$ | OSR$\\uparrow$ | SR$\\uparrow$ | SPL$\\uparrow$ | NE$\\downarrow$ | OSR$\\uparrow$  | SR$\\uparrow$   | SPL$\\uparrow$  |\n| 1   | X                 | ✓                        | ✓        | ✓    | 5.34           | **62.1**      | **52.9**     | **45.7**      | **6.22**       | **48.9**       | **40.1**       | **28.5**       |\n| 2   | X                 | X                        | ✓        | ✓    | 7.59           | 43.9          | 32.7         | 21.1          | 8.76           | 27.4           | 19.3           | 11.4           |\n| 3   | X                 | ✓                        | X        | ✓    | 5.77           | 54.7          | 47.9         | 43.3          | 6.58           | 38.5           | 26.8           | 21.0           |\n| 4   | X                 | ✓                        | ✓        | X    | **5.26**       | 61.8          | 52.4         | 45.7          | 6.37           | 46.2           | 39.3           | 26.2           |\n| 5   | X                 | ✓                        | X        | X    | 5.63           | 51.1          | 45.7         | 40.2          | 7.89           | 34.8           | 25.7           | 17.8           |\n| 6   | ✓                 | X                        | ✓        | ✓    | 6.14           | 51.7          | 43.1         | 33.9          | 7.85           | 35.9           | 28.4           | 21.8           |\n\n**Table 1. More experiments for ablation study.**\n\n\n### **1. The contribution of rendered panoramic 3D tokens, instance tokens, and zone tokens.**\n As shown in Table 1, the removal of rendered panoramic patch tokens (Row 2), instance tokens (Row 3), zone tokens (Row 4), or both instance and zone tokens (Row 5) from the full model (Row 1) lead to performance drops of varying degrees. **The impact on performance follows the order: patch > instance > zone tokens**.\n\n### **2. The significance of complex representation designs, such as rendering panoramic tokens using g3D-LF and obtaining instance-level representations via FastSAM. Their effectiveness for pre-exploration and lifelong memory.**\nAs analyzed in VLN-3DFF[1], rendered panoramic patch tokens substantially mitigate the limited field of view caused by monocular cameras on most robots. **Comparing Rows 1 and 6 in Table 1, replacing rendered panoramic tokens with CLIP patch features extracted from the current monocular view** results in about a **10% drop** in navigation success rates on both R2R-CE and REVERIE-CE. Moreover, lifelong memory experiments in Table 3 of the original submission show that tokens rendered from feature fields have greater potential to leverage pre-exploration (although the comparison is not entirely fair)  and lifelong memory for performance improvement than the methods that directly extract patch-level features from current observations.\nFor obtaining instance-level tokens via FastSAM, rows 1 and 3 in Table 1 demonstrate the performance gains brought by instance tokens. Moreover, instance-level representations are highly valuable for object-centric tasks in large-scale scenes when extending 3D-VLMs, such as instance grounding and mobile manipulation, although Dynam3D has not yet attempted such applications.\n\n### **3. The heterogeneity of different types of tokens, such as patch, instance, and zone tokens, does not overly confuse the model learning.**\nAlthough we do not explicitly define learnable token-type embeddings, each token type has its own positional encoding network. This allows the 3D-VLM to distinguish between different token types. Additionally, during representation pretraining, patch, instance and zone tokens are aligned or distilled into the CLIP semantic space using corresponding language and 2D visual features at varying granularities. Despite sharing the same semantic space, each token type exhibits distinct semantic granularities.\n\n### **4. Merging Discriminator Accuracy.**\nEvaluated on MP3D scenes from the R2R-CE Val Unseen split, the merging discriminator achieves **78.6%** accuracy in determining the merger of 2D and 3D instances. Although not perfect, the frustum culling strategy removes most near-field old instances, leaving only instances near the view boundaries and at a distance to be merged. Thus, the current accuracy is sufficient to effectively support instance token updates.\n\n### **5. More details on the processing of VLN data.**\nAs detailed in the Supplementary Material (Line 503 of the original submission), we use a pre-trained waypoint predictor[2] to generate candidate waypoints and select the GT waypoint closest to the ground-truth (GT) path and destination via the simulator API. The agent can achieve **96 SPL and nearly 100% success rate (SR)** on R2R-CE Val Unseen when it strictly **follows these GT waypoints**. This provides reliable supervision for training. Each next GT waypoint yields a heading angle (in 15° increments) and a forward distance (in 25 cm increments), which are used as GT textual action supervision for the 3D-VLM model.\n\n[1] Wang et al. Sim-to-real transfer via 3d feature fields for vision-and-language navigation. In 8th Annual Conference on Robot Learning, 2024.\n\n[2] Hong et al. Bridging the gap between learning discrete and continuous environments for vision-and-language navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022."}, {"review_id": "iLILeGoVNz", "reviewer": "Reviewer_kSWU", "summary": "This paper proposes a new 3D hierarchical representation structure for organizing information in vision-language action (VLA) models. The core idea is inspired to some extent by mapping-based methods, but it also departs from them in important ways. The proposed structure acts as a sliding-window-style, multi-level, dynamically updated, fused, and removed semantic memory, rather than a persistent global map. This design addresses key issues faced by video-based VLA models, namely, shallow physical understanding and inefficient memory organization, as the authors themselves note in the intro second paragraph. Technically, the paper introduces a three-tier 3D representation and its corresponding update/fusion mechanism, and demonstrates its effectiveness through both simulator-based and real-world robot experiments.", "questions": "I would appreciate it if the authors could primarily address the two major weaknesses:\n\nGeneralization: Please consider adding cross-dataset evaluation or using previously unseen real-world environments (e.g., outdoor scenes) to verify the robustness of 3D-language alignment.\n\nInference Speed: It would be highly valuable if the authors could propose insightful and practical ways to reduce inference latency. If the inference can be sped up noticeably, it would significantly strengthen the paper.\n\nIf possible, it would also be great to see the authors touch on the remaining concerns—about demo quality, Section 4.2 clarity, and action format—even if only in discussion or qualitative analysis.", "limitations": "Yes, the limitations are discussed.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths \n\n1、This is a strong paper with a clear motivation and solid technical contributions. The proposed direction feels like a natural and promising step forward given the current state of research, bringing 3D awareness into VLA without incurring excessive computational or memory overhead.\n\n2、The technical design of this work is carefully crafted. It introduces a multi-level 3D representation structure, and incorporates dynamic mechanisms for updating, merging, and pruning tokens, which ensures computational efficiency and a certain degree of real-time capability. The technical components are well-formulated with clear mathematical definitions, allowing readers to precisely understand the authors' intentions.\n\n3、The experiments are thorough, and the comparisons are both relevant and novel. The accompanying demo, though simple, is convincing. Importantly, the insights presented in this work could inspire further research in the community, it is a meaningful exploration.\n\nWeaknesses\n\n1、My main concern lies in generalization. Higher-dimensional representations typically require more data to train effectively. Existing VLA systems like Uni-NaVid are trained on massive 2D-scale datasets (millions to tens of millions of samples), whereas this work does not appear to increase the data volume in proportion to the added complexity of 3D representations. This raises the question of whether the proposed 3D-language alignment generalizes well, especially under real-world conditions with noise and the inherent sim-to-real gap. I strongly encourage the authors to conduct more experiments specifically targeting generalization, either within simulators (e.g., cross-scene or cross-dataset setups) or on real robots using unseen environments, such as outdoor scenes.\n\n2、The second major issue is inference speed. As shown in Section 4.4, the real-world inference runs at under 2 FPS, which is somewhat slow for practical deployment. Interestingly, as noted in Section 4.1, the model's parameter size is actually smaller than other VLA baselines. This suggests that there may be unexplored opportunities—either at the model level or in engineering—to significantly improve runtime performance. I hope the authors can propose concrete and actionable ideas to reduce inference latency.\n\n3、A third point is that the robot demo leaves room for improvement. Given the introduction of 3D spatial understanding, one would expect the system to showcase unique VLN capabilities that go beyond existing 2D-based models. More spatially grounded or fine-grained instructions could have made the demo more impressive. As it stands, it feels somewhat ordinary.\n\n(Minor concern) The intent of Section 4.2 is a bit unclear. The proposed representation is described as \"ephemeral and disposable,\" yet the experiments on pre-exploration and lifelong memory imply persistent memory reuse. It would be helpful for the authors to clarify this apparent contradiction and explicitly state the purpose of these experiments.\n\n(Minor concern) Regarding the action prediction format in VLA models—have the authors considered using more advanced policy structures, such as multi-step forecasting or diffusion-based policies? Given the richness of the spatial representation, such approaches could lead to more foresightful and smoother navigation.", "was_revised": true, "final_justification": "This is a fairly good piece of work, with no obvious flaws or weaknesses. I believe it meets the bar for NeurIPS acceptance, so I am keeping my original score.", "rebuttal": "Thanks for your insightful comments and suggestions!\n## **1. Analysis of Dynam3D's Generalization Capability**\n### **1) 3D Representation Generalization**\nAs detailed in the Supplementary Material (Line 503 of the original submission), we follow SceneVerse and g3D-LF to collect over 5K scenes with 2M language annotations from ScanNet, HM3D, Matterport3D, 3RScan, ARKitScenes, and Structured3D. This data is used to train our Dynam3D representation model and retrain the g3D-LF feature field model. HM3D, Matterport3D, and Structured3D are from simulators or renderers. The remaining training data ScanNet, 3RScan, and ARKitScenes provide over 2.5K scenes captured using real RGB-D cameras in real-world environments. This helps eliminate the sim-to-real gap and ensures strong generalization of the learned representations to real-world scenarios.\n\n### **2) VLN Model Generalization**\nAs detailed in the supplementary materials (Line 503 of the original submission), we conducted mixed training across 861 environments (HM3D, MP3D) to enhance the navigational generalization of our Dynam3D. This involved over 4 million navigation samples, comprising both model-generated data (ScaleVLN, NavRAG; 4M+) and human-annotated data (R2R-CE, REVERIE-CE; 20K+). This diverse dataset included various navigation instructions, such as step-by-step following, high-level description, and user-demand instructions.\nTo further validate the generalization performance of our Dynam3D, we conduct zero-shot evaluations on the **HM3D-OVON** Val Unseen dataset for open-vocabulary object navigation to show the cross-dataset performance and zero-shot generation:\n| Methods   | SR   | SPL                              |\n| --------- | ---- |:--------------------------------:|\n| VLFM      | 35.2 | 19.6                             |\n| Uni-NaVid | 39.5 | 19.8                             |\n| Dynam3D   | **42.7** | **22.4** |\n\n## **2. Optimize inference latency with better action prediction policies**\nThe dynamic 3D-VLM contexts arising from real-time updates of rendered panoramic patches, instance and zone tokens preclude the application of static-context inference acceleration techniques such as KV caching that is commonly employed in traditional video-based VLMs. However, as suggested by Reviewer kSWU, our Dynam3D has the potential to accelerate the navigation process using multi-step forecasting or diffusion-based policies by using high-quality 3D-aware representations. For example, a large 3D-VLM can execute **high-level encoding and reasoning at 2 FPS**. A **smaller action expert model** can use latent features from the 3D-VLM (similar to the VLA model π₀) as input to predict multiple consecutive navigation trajectory points at **over 20 FPS**. During robot movement, the **3D representation is continuously updated** in real time at **over 10 FPS**. Currently, our Dynam3D still uses text output for action prediction primarily to ensure a fair comparison with previous video-based models that also used text output. However, this navigation action output mode should only be a temporary solution. Once sufficient training data is available, smoother and faster navigation can be achieved with the better choice of predicting multi-step continuous trajectory points.\n\n## **3. Better demos to showcase spatial reasoning ability**\nThank you for the suggestion. As NeurIPS does not support media uploads this year, we are unable to include additional demos. Nevertheless, our Dynam3D exhibits clear advantages over video-based models in 3D perception, localization, and reasoning with better 3D-aware representations. If paper is accepted, we will provide more demonstrations in the camera-ready version to highlight its spatial reasoning capabilities and potential in object-centric tasks such as mobile manipulation, which require precise object localization and geometric understanding.\n\n## **4. Dynamic Representation and Lifelong Memory in Section 4.2**\nThank you for your suggestion. In our Dynam3D, the robot discards outdated representations within its current field-of-view and adds or updates new ones to ensure real-time understanding of dynamic environments with each new observation. However, this does not conflict with its advantage of maintaining lifelong memory. Features corresponding to regions outside the robot’s current view of the robot are still preserved and not updated, and serve as long-term memory to support navigation. This is due to the limited field of the monocular camera, where the majority of the scene is preserved since it lies outside the current view of the robot. These features are retained until new observations covering those regions are available to update them. This highlights the ability of our Dynam3D to balance real-time adaptability in dynamic environments with the effective use of long-term memory."}, {"review_id": "J1BuNpJ8DO", "reviewer": "Reviewer_CAAU", "summary": "This paper introduces Dynam3D, a system designed to help robots follow natural language instructions and navigate through indoor environments using only a single RGB-D camera. Unlike previous methods that rely on video-based models, Dynam3D builds a dynamic 3D representation of the environment using a layered structure that includes small visual patches, object instances, and larger spatial zones. It updates this representation in real time, allowing the system to better understand the 3D layout, remember past observations, and adapt to changes like moving objects. The authors also introduce a 3D Vision-Language Large Model  to predict the action in a continuous environment. Experiments show that Dynam3D achieves strong results on several navigation benchmarks and works well in real-world and dynamic settings.", "questions": "How would the performance be affected if the camera parameters are miscalculated or the depth data contains noise?\n\nCompared with discrete VLN settings, what are the strengths and limitations of VLN in continuous environments? It is advisable to clarify this in the paper.", "limitations": "The authors have not adequately discussed the application scenarios of the algorithm and the requirements for data quality.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\nThe hierarchical 3D representation enables navigation in dynamic scenes by updating only the instance-level features, allowing the algorithm to adapt its behavior in response to moving objects.\n\nThe authors applied this method to a real robot and demonstrated its performance in real-world operation.\n\n**Weaknesses**\n\nThe reviewer thinks the biggest weakness of this paper is the lack of a comprehensive ablation study. For example, while the method outperforms previous approaches in the experiments, it's unclear whether the comparison was made under the same model parameter size. In real-world applications, RGB-D cameras produce different types of noise depending on the scene, and the impact of such depth noise on navigation has not been sufficiently analyzed. Moreover, the point clouds shown in the paper appear almost perfect, so the reviewer has reservations about how well the algorithm would perform in real-world scenarios with noisy data.\n\nThe pipeline of this algorithm is highly hand-crafted, which may lead to poor robustness in handling certain edge cases, such as camera tracking errors or segmentation mistakes that occur during intermediate stages of the pipeline.", "was_revised": true, "final_justification": "The authors’ rebuttal addresses most of my concerns, so I am increasing my score. Although the pipeline remains rather hand-crafted and complex—which may limit its broader applicability and weigh on the evaluation—I still consider the work suitable for acceptance as a conference paper.", "rebuttal": "Thanks for your valuable comments.\n## **1. Model Parameter Size Analysis**\nAs shown in Section 4.1, our Dynam3D has an advantage in terms of parameter size compared to major large model-based VLN methods: **NaVid 7B, Uni-NaVid 7B, and Dynam3D 3.8B**. As shown in Tables 1 and 2 of the original submission, our Dynam3D achieves stronger performance with the smaller parameter scale.\nFor more ablation study analysis to more clearly demonstrate the performance contribution of each component in Dynam3D, please refer to our response to Reviewer QXMr.\n\n## **2. Robustness Analysis to Noise**\nThanks for your insightful suggestions. As demonstrated by the real-world experiments in Section 4.3 of the original submission, our Dynam3D exhibits excellent performance in real-world navigation based on the Stretch 3 robot. This reflects the robustness of our Dynam3D in real-world environments. A **ROS Nav2-based SLAM system** is developed for **precise localization and pose estimation** on the Stretch 3 robot by combining sensory data from **LiDAR, IMU, and a depth camera**. Although no official data is available, user reports and our rough estimates suggest a **positioning accuracy of approximately ±5 cm and an orientation accuracy of about ±3 degrees**.\nFor the **Intel RealSense D435i depth camera** used in Stretch 3 robot, its official specifications indicate a depth error of **approximately ±4 cm within a 2-meter range and ±6 cm within a 3-meter range**.\nTo further validate the practical impact of this noise on navigation performance, we add different types of noise to the simulator and evaluate the performance of our Dynam3D on R2R-CE Val Unseen in noisy environments.  In order to simulate SLAM noise, we introduce localization noise, uniformly sampled from -5 cm to +5 cm, and orientation noise, uniformly sampled from -3 degrees to +3 degrees, to the agent's position at each simulation step. To simulate depth camera noise, we add random noise to the depth maps acquired from the simulator. For depths within 2 meters, noise is sampled between -4 cm and +4 cm; for depths at 2~3 meters, noise is sampled between -6 cm and +6 cm; for depths over 3 meters, noise is sampled between -10cm and +10 cm.\n| SLAM Noise | Depth Noise | NE$\\downarrow$ | OSR$\\uparrow$ | SR$\\uparrow$ | SPL$\\uparrow$ |\n|---|---|---|---|---|---|\n| × | × | 5.34 | 62.1 | 52.9 | 45.7 |\n| √ | × | 5.46 | 61.3 | 52.1 | 45.0 |\n| × | √ | 5.39 | 61.9 | 52.3 | 45.2 |\n| √ | √ | 5.47 | 60.7 | 50.9 | 44.1 |\n\nAs shown in the table above,**even with the simultaneous addition of simulated SLAM noise and depth noise, the navigation success rate (SR) only decreased by approximately 2%** (comparing the last row to the first row), demonstrating Dynam3D's robustness to noise. A significant reason is, unlike point cloud-based 3D-VLM models require near-perfect point clouds for their point cloud encoder to extract semantics based on texture and fine geometry, **our Dynam3D leverages 2D foundation models (e.g., CLIP, FastSAM) to extract visual semantic features (patch-level), which are unaffected by SLAM and depth noise**. Our Dynam3D acquires 3D-aware representations through a hierarchical encoding of patch-instance-zones, even with minor spatial position deviations due to noise, this strategy remains sufficient for perceiving spatial geometry and scene layout at the object and zone levels.\n\n## **3. Comparing VLN in Discrete and Continuous Environments**\nIn the discrete environment VLN, a predefined navigable connectivity graph with nodes is used. The agent can only move between these nodes in the graph and is typically equipped with a panoramic RGB-D camera. Due to the limitations of the computational resources and model capacity in few years ago, discrete environments significantly simplify the navigation process for easier research. For example, it eliminates the need for low-level action prediction, ignores collision avoidance, and disregards potential environmental changes.\n\nHowever, a significant drawback is that navigation models trained in discrete environments are almost impossible to deploy on real robots due to two main reasons: 1) Real-world environments rarely have perfectly predefined navigation connectivity graph, and 2) The vast majority of robots do not possess panoramic RGB-D cameras due to their large size and high cost.\nIn contrast, continuous environments (VLN-CE) , especially with a monocular camera setting, show greater practical value. Although VLN model performance on continuous environment benchmarks (R2R-CE, REVERIE-CE) has not yet surpassed that of discrete environments (R2R, REVERIE), the gap for real-world deployment is significantly reduced. The continuous settings require models to predict low-level actions, e.g. rotation and forward movement, and handle collision avoidance. **What's more, continuous VLN largely mitigates a key limitation, i.e. discrete VLN models tend to overfit to perfect connectivity graphs, which severely restricts their generalization in real-world navigation**.\n\n## **4. Discussion for the application scenarios of Dynam3D and the requirements for data quality.**\nDynam3D is designed for language-guided indoor navigation tasks carried out by robots equipped with precise SLAM and monocular RGB-D cameras, and is capable of adapting to real-time human and object movements. \n\nFor data quality, posed RGB-D images are required as model input, the specific accuracy requirements for depth images and camera poses can be found in the **Robustness Analysis to Noise** section."}]

Author rebuttal:

Meta-review: Summary: 
This paper presents Dynam3D, a dynamic layered 3D representation framework for vision-and-language navigation. It integrates patch, instance, and zone tokens with hierarchical updates, enabling robust spatial understanding, long-term memory, and adaptability to dynamic environments. The method achieves state-of-the-art results across multiple benchmarks and demonstrates strong real-world robot performance.

Strengths:
The technical design is solid and innovative: it brings hierarchical 3D awareness into VLN with carefully engineered dynamic updates. Experiments are extensive, covering both simulation and real-world settings, and show clear gains over baselines. The work is impactful and addresses practical deployment.

Weaknesses: 
The pipeline is somewhat complex and training is resource-intensive, and questions remain about scalability outdoors and efficiency. However, the authors’ rebuttal addressed these concerns with convincing clarifications and ablations.

Overall, this is a technically strong and comprehensive contribution with clear empirical value. ACs recommend acceptance.
Decision: Accept (oral)