Title: ProDyG: Progressive Dynamic Scene Reconstruction via Gaussian Splatting from Monocular Videos
Venue: NeurIPS 2025
Accepted: yes
Keywords: Dynamic

scene reconstruction

dynamic scene reconstruction

4D reconstruction

Gaussian Splatting

SLAM

monocular

dynamic SLAM

pose estimation

mapping

tracking
Abstract: Achieving truly practical dynamic 3D reconstruction requires online operation, global pose and map consistency, detailed appearance modeling, and the flexibility to handle both RGB and RGB-D inputs. However, existing SLAM methods typically merely remove the dynamic parts or require RGB-D input, while offline methods are not scalable to long video sequences, and current transformer-based feedforward methods lack global consistency and appearance details. To this end, we achieve online dynamic scene reconstruction by disentangling the static and dynamic parts within a SLAM system. The poses are tracked robustly with a novel motion masking strategy, and dynamic parts are reconstructed leveraging a progressive adaptation of a Motion Scaffolds graph. Our method yields novel view renderings competitive to offline methods and achieves on-par tracking with state-of-the-art dynamic SLAM methods.

ProDyG: Progressive Dynamic Scene Reconstruction via Gaussian Splatting from Monocular Videos

Abstract

Achieving truly practical dynamic 3D reconstruction requires online operation, global pose and map consistency, detailed appearance modeling, and the flexibility to handle both RGB and RGB-D inputs. However, existing SLAM methods typically merely remove the dynamic parts or require RGB-D input, while offline methods are not scalable to long video sequences, and current transformer-based feedforward methods lack global consistency and appearance details. To this end, we achieve online dynamic scene reconstruction by disentangling the static and dynamic parts within a SLAM system. The poses are tracked robustly with a novel motion masking strategy, and dynamic parts are reconstructed leveraging a progressive adaptation of a Motion Scaffolds graph. Our method yields novel view renderings competitive to offline methods and achieves on-par tracking with state-of-the-art dynamic SLAM methods. Project page: cs-vision.github.io/ProDyG.github.io

1 Introduction

Dynamic scene reconstruction is fundamental to problems like action recognition, scene understanding, autonomous driving, robotics and augmented reality, because it provides a temporally consistent spatial understanding of how objects and agents move and interact in their environment an essential prerequisite for any system to perceive, predict, and act in the world. This problem has been tackled in various ways e.g. as an online SLAM task [4, 89, 34], an offline reconstruction task [29, 73, 79, 39, 56, 55], and lately as a feedforward task [86, 87, 11, 69]. However, most dynamic SLAM works ignore the dynamic parts [89, 4], only reconstructing the static world, only track rigid objects [85, 43] or are restricted to object-centric reconstruction [45, 58]. Offline methods typically separate the reconstruction task into pose estimation followed by reconstruction [55, 56], or are not scalable to long input videos due to their reliance on global optimization over all past frames [29, 73]. Feed-forward methods train large transformers for online dynamic scene reconstruction, but are yet to achieve global pose consistency and only produce point clouds [69, 11, 86].

Despite recent progress, existing methods fall short of at least one of the requirements for practical dynamic scene reconstruction: (1) online operation , tightly coupling pose estimation and dense map reconstruction for scalability; (2) global pose and map consistency ; (3) expressive representations like 3DGS [23] for detailed appearance and geometry; and (4) flexibility to handle both RGB and RGB-D input. SLAM systems often ignore dynamics or lack detail; feedforward methods trade consistency and accuracy for speed; and offline methods are challenging to scale.

We propose ProDyG 1 , a method for Pro gressive, Dy namic scene reconstruction with G aussians from monocular input, that meets all four criteria. Our contributions are:

Online RGB Stream

ProDyG

t

Legend

Est. Camera Trajectory

Dyn. Gaussian

Trajectories

Camera Tracking & Dynamic Scene Reconstruction

A motion mask prediction strategy, using dynamic flow and refined with semantic point prompting [50]. This is integrated into the SLAM backend for robust pose estimation.

An online dynamic reconstruction pipeline that uses Motion Scaffolds [29] to propagate 3DGS in space and time.

Support for both RGB and RGB-D, with ProDyG as the first online RGB-only method.

Competitive novel view synthesis results against state-of-the-art offline methods.

2 Related Work

Dynamic Dense SLAM. Dynamic dense SLAM methods mainly address two scenarios: filtering dynamic content to reconstruct only static environments [4, 13, 90, 89, 77, 19, 15, 81, 44, 5, 74, 59, 48, 57, 82, 37, 32, 88, 1, 24, 7, 64, 26, 72, 51], or explicitly tracking and reconstructing dynamic objects alongside static structures [85, 3, 43, 17, 52, 53, 78, 14, 45, 58, 8, 65, 34, 35, 31]. Basic strategies for handling dynamic elements include outlier filtering, robust loss functions [90, 44, 5], or covariance scaling [1], but these fail with extensive dynamic content. Instead, a motion mask is estimated with optical flow [88, 82], semantic segmentation [26, 14, 35, 57, 4, 33, 77, 51, 81, 74, 85, 59, 72, 53] or a combination of the two [34, 19, 15, 52], with uncertainty-guided loss functions [89, 37], with motion segmentation networks [57], point tracking [65], unsupervised clustering [17] or via conditional random fields [32]. In many real-world scenarios, reconstructing dynamic objects is critical. Some traditional works track rigid dynamic content during SLAM [85, 3, 43, 17, 52, 53, 78, 14] or perform object-centric non-rigid reconstruction [45, 58, 8]. While the above-mentioned methods can reconstruct dynamic point clouds [4, 85, 74, 59, 88, 17], surfels [52, 53], oct-trees [81] or signed distance functions [48, 45, 58, 8], they struggle to model photometric details and lighting effects needed for scene understanding and photorealistic rendering. In response, neural implicit methods [33, 77, 51, 19] have been proposed, but they are too slow for real-time SLAM. Dynamic 3DGS [23] SLAM has emerged as a solution [15, 34, 35, 31, 89, 32, 72, 26] with concurrent works such as DynaGSLAM [34] using dynamic scene flow from optical flow to update the Gaussian means, [65] adapts [79] to the online setting and [35] uses an MLP to deform the dynamic parts. These works cannot handle pure RGB input. For a recent survey on dynamic SLAM, we refer to [71].

Dynamic 3D Reconstruction. Dynamic 3D reconstruction shares many similarities with dynamic SLAM, but videos are processed offline [29, 73, 79, 9, 21, 80, 27, 28, 36, 40, 41, 49, 60, 84, 2, 16, 39, 25, 91, 18, 38, 68]. These works typically implement a strategy for deforming dynamic content over time. Some optimize a deformation field via an MLP [2, 60, 40, 80, 25, 73], use motion basis functions [16, 49, 68, 36, 29, 28, 27, 21, 39, 18], or extend 3D Gaussian Splatting [23] with a time attribute [79, 9]. Due to the ill-posed nature of dynamic reconstruction, strong priors are commonly used, such as point trackers, optical flow, and regularizers like as-rigid-as-possible (ARAP), constant velocity, and acceleration [29, 41, 56, 68, 39]. Inductive biases also help, such as MLP smoothness

Reconstruction

Dynamic

Static

Tracking

Non-Keyframes based

Keyframes based

Input

Online RGB-Video

Keyframe

Selection

DBA

RAFT

Optical Flow

Con

fi

dence

Weights

Disparity

Camera

Poses

Normalized Mean

Residual Flow

Keyframes

Thresholding

Coarse Motion

Mask

Conf. Weight

Suppression

&

Photometric

Optimization

Motion Mask Loss

Geometry

Optimization

&

Photometric

Optimization

Non-Keyframe Camera Poses & Depths

Keyframes

Non-Keyframe

DBA

Depth

Reprojection

Monocular

Depth

Model

Depth

Camera

Poses

CoTracker3

2D

Tracks

2D Tracks to 3D Tracks Lifting

Non-Keyframes

Motion

ff

olds

+

Dynamic

Gaussians

+

optimized Motion Sca

ff

olds

optimized Dynamic Gaussians

optimized Static Gaussians

Fine Motion

Mask

Fixed models

Optimization Layers

Algorithms

Ftr

Ft

Ft

+

and continuity, or selecting a sparse set of motion bases. While some of these works use SLAM for pose estimation, reconstruction is a post-processing step [55, 56].

Feed-forward Methods. Feed-forward methods inspired by DUSt3R [70] and MASt3R [30] have been proposed to solve dynamic 3D reconstruction. These works rely on large-scale training of transformer networks and can infer pointmaps from dynamic input image pairs [86, 87, 11, 13, 6, 69]. However, they typically entangle static and dynamic points [69, 86], meaning that the motion of dynamic points cannot be tracked over time. In concurrent work, [11, 13, 87] propose to enable 3D correspondence estimation of dynamic 3D points. DAS3R [75] learns to predict motion masks building on MonST3R [86], and only predicts the static world, using 3DGS [23]. Common to all feed-forward methods is that they process the video either in image pairs or in a sliding window, without guaranteeing global pose and map consistency, contrary to SLAM. In contrast, we perform globally consistent and online dynamic reconstruction using SLAM.

3 Method

ProDyG is an online dynamic dense mapping and tracking system that robustly tracks a monocular camera (sec. 3.1) while also reconstructing and disentangling the static and dynamic parts (sec. 3.3) with a 3D Gaussian Splatting representation (sec. 3.2). For an overview, see fig. 2.

3.1 Motion-Agnostic Online Camera Tracking

Flow-Based Robust Camera Tracking. We employ Splat-SLAM [54] as our tracking backend, i.e. we maintain a factor graph storing camera extrinsics ω i , disparity estimates d i ∈ R ( H × W ) × 1 per keyframe (node) i , and optical flow ˜ p ij -p i ∈ R ( H × W × 2) × 1 per edge ( i, j ) , where p i is the flattened pixel grid from keyframe i , and ˜ p ij represents the flattened predicted pixel coordinates when p i is projected into keyframe j using optical flow. We also store the confidence w ij ∈ R ( H × W ) × 1 associated with the optical flow. Tracking is achieved following Dense Bundle Adjustment (DBA) [66], where the keyframe poses ω and disparities d are optimized with a reprojection error:

(b) Mean Mag. ¯ r i of Residual Flow

Here, E are the edges of a local factor graph, applied in a sliding window manner, K is the camera intrinsics, ∥ · ∥ Σ ′ ij denotes the Mahalanobis distance with confidence weights suppressed by a coarse binary motion mask C i , defined as 1 where motion is detected and ¯ C i stands for negation of C i .

We generate the coarse motion masks { C i } as follows. After each DBA iteration, we compute the residual flow ˆ r ij = (˜ p ij -p i ) -( p ij -p i ) = ˜ p ij -p ij by subtracting the camera-induced flow p ij -p i from the estimated optical flow ˜ p ij -p i . This residual is near zero in static regions and larger in dynamic ones. Dynamic areas are identified by evaluating the normalized mean magnitude ¯ r i ( x, y ) of ˆ r ij over the connected target keyframes N i for each keyframe i

For each keyframe, C i is computed by thresholding ¯ r i ( x, y ) at the top 20% , assigning zero weight to potentially dynamic regions during DBA. As ¯ r i ( x, y ) is updated each DBA iteration, it is progressively refined, enabling robust pose estimation in dynamic environments.

Semantic-Guided Motion Mask Refinement. To improve the separation of dynamic and static regions for reconstruction (sec. 3.3), we use SAM2 [50] to generate fine-grained motion masks for every frame. We show an example visualization of the input image, the mean magnitude ¯ r i of the residual flow, the coarse motion mask, and the fine motion mask in fig. 3.

Our approach has two phases: initialization and incremental prediction. During initialization, we generate fine motion masks by prompting SAM2 at the centroids of connected regions in the medianfiltered coarse masks C i . During incremental prediction, we extend existing object-wise masks into new frames and add new segmentations based on the coarse masks. We validate each segmentation by counting prompt point candidates to filter false positives. Before this phase, we adjust the threshold (top 20% at initialization) based on the dynamic pixel ratio in the latest fine motion mask, adapting to sequence-specific motion. Detailed algorithms are in the supplemental material.

With the fine motion masks { M i } , we further enhance our tracking robustness by replacing the coarse mask C i with the fine during DBA (eq. (1)) as Σ ′ ij = diag ( w ij (1 -M i )) . This semantic-guided approach provides more accurate exclusion of dynamic distractors for subsequent iterations of DBA, including local BA, global BA and loop BA [54].

3.2 Static and Dynamic Map Representation

We represent the scene map G = {G s , G d ( t ) } as a set of 3D Gaussians [23], split into static G s = { g i } N s i =1 and time-varying dynamic components G d ( t ) = { g i ( t ) } N d i =1 . Each Gaussian-whether static or dynamic-is parameterized by a mean µ i ∈ R 3 , rotation matrix R i ∈ R 3 × 3 , scale s i ∈ R 3 , opacity o i ∈ [0 , 1] , and RGB color c i ∈ R 3 . Rendering is performed by first projecting the 3D Gaussians onto the image plane, approximated as 2D Gaussians. The 2D opacity at a pixel p is then

where µ ′ i = Kω -1 µ i with ω the camera-to-world pose, K the camera intrinsics, and Σ ′ i = P Σ i P ⊤ = P ( R i S i S ⊤ i R ⊤ i ) P ⊤ , with S i = diag( s i ) and P the affine projection [92]. Given the pose

ω , we render a pixel p following the 3DGS [23] pipeline for RGB images C , RaDe-GS [83] for depth maps D r , and additionally compute opacity maps O as

Here, K is the set of Gaussians projected to p . We use the unbiased RaDe-GS rasterizer, which replaces the z-buffer depth with ˆ d i , the ray-Gaussian intersection depth along the viewing ray.

3.3 Progressive Dynamic Scene Reconstruction

We propose a progressive approach to dynamic scene reconstruction, which enables online processing and allows the system to adapt to the continuously evolving scene geometry and camera motion. ProDyG uses the same keyframe selection strategy as in Splat-SLAM [54], and the addition of new keyframes triggers progressive reconstruction of both static and dynamic regions. To achieve global map consistency, we apply 3DGS map deformations for static regions followed by photometric optimization as in [54]. For dynamic Gaussians, we extend the Motion Scaffold representation [29] to handle incremental updates. Next, we review the necessary steps.

Non-Keyframe Camera Poses and Depths. Dynamic scene reconstruction benefits from temporally dense supervision to capture smooth motion, yet the SLAM backend [54] operates on sparse keyframes for efficiency. To bridge this gap, we derive the camera poses and depth maps for non-keyframes that exist between keyframes.

To obtain non-keyframe camera poses, we follow the practice of DROID-SLAM [66]. For each nonkeyframe F t positioned between two neighboring keyframes F t 1 and F t 2 , we construct a temporary local graph with edges connecting both keyframes to F t . Wethen execute the recurrent update operator and the Dense Bundle Adjustment (DBA) layer on this graph, which optimizes the non-keyframe poses based on the already optimized keyframe poses.

For non-keyframe depth estimation, we leverage reprojection from neighboring keyframes. Given a non-keyframe F t with its derived camera-to-world pose ω t and neighboring keyframes F t 1 and F t 2 with poses ω t 1 and ω t 2 , we first back-project the pixels of both keyframes into 3D:

where K is the camera intrinsic matrix, and d t 1 and d t 2 are the disparities of keyframes F t 1 and F t 2 , respectively. We then reproject all back-projected 3D points p 3D t 1 ,t 2 = p 3D t 1 ∪ p 3D t 2 from both keyframes to the camera of the non-keyframe F t and record their distance to the camera along the z-axis:

We filter out points that are back-projected from dynamic pixels or using invalid disparity estimates (determined by a multi-view consistency check as in [54]). Using the remaining reprojected 2D coordinates and their corresponding depth values, we bilinearly interpolate to create a reprojection depth map D repro t . Finally, to get dense depth, we align a monocular depth estimate D mono t with D repro t by estimating scale θ t and shift γ t parameters through least squares fitting:

Motion Scaffolds. We adopt Motion Scaffolds [29] (MoSca) as our dynamic representation. MoSca a structured graph ( V d , E d ) representing the underlying motion of the scene, where each node v ( m ) ∈ V d encodes the motion trajectory of a specific region. Each MoSca node v ( m ) is defined as

where Q ( m ) t ∈ SE (3) represents the per-timestep rigid transformation at time t , and r ( m ) is an RBF radius parameterizing the node's influence. These nodes are initially anchored by lifting 2D pixel trajectories using the estimated camera poses ω t and depth maps D aligned t computed in eq. (7).

Similar to the static map deformation [54], we also deform the dynamic Gaussians to reflect the continuously updated pose and depth estimates from the SLAM backend. Each dynamic Gaussian is defined relative to a MoSca node (not in world coordinates) and by re-anchoring the MoSca nodes, the dynamic Gaussians are updated accordingly. The MoSca nodes are re-anchored in 3D when new pose and depth updates are available. Thus, ProDyG achieves global dynamic map consistency prior to optimization. This ensures better convergence and consistency between the static and dynamic components of the scene.

Following MoSca [29], the set of dynamic Gaussians G d ( t ) at any query timestamp t is formed by warping each individual Gaussian g i ( t ref i ) = ( µ i , R i , s i , o i , c i ; t ref i , ∆ w i ) from its reference timestamp t ref i (the timestamp where it is initialized) to t

Here, w ( · ) is the base RBF skinning weight parametrized by { r ( m ) } m ∈E d ( m ∗ ) , where the neighborhood E d ( m ∗ ) consists of the nearest MoSca node v ( m ∗ ) and all nodes connected to v ( m ∗ ) , and ∆ w i are learnable per-Gaussian skinning weight corrections. The warping function W ( · ) is computed using Dual Quaternion Blending (DQB) [22]:

where ∆ Q ( m ) = Q ( m ) t dst ( Q ( m ) t src ) -1 is the relative transformation between t src and t dst for node m .

Progressive Construction of Motion Scaffolds. After the initial bootstrapping phase of the SLAM backend, we initialize the Motion Scaffolds and dynamic Gaussians. First, we employ CoTracker3 [20] to generate dense long-term 2D pixel trajectories within the fine motion masks { M i } (sec. 3.1) and corresponding per-timestamp visibility labels. Similar to [29], these 2D trajectories are first lifted into 3D space at visible timestamps using the camera poses and depth maps estimated by the backend, while we linearly interpolate between nearby observations at invisible timestamps. Finally, we sample a subset of the lifted 3D tracks to serve as the initial positions for the MoSca nodes.

Subsequently, we carry out geometry optimization on the initialized MoSca nodes as described in [29] to infer rotations and positions of invisible nodes, minimizing the as-rigid-as-possible (ARAP) loss, velocity consistency loss and acceleration consistency loss.

After the geometry optimization of the Motion Scaffolds, we initialize the dynamic Gaussians at 3D positions obtained by back-projecting pixels within the fine motion masks. Finally, we perform a photometric optimization of both the Motion Scaffolds and the dynamic Gaussians using a combination of losses following [29]: an RGB loss L rgb that enforces color consistency, a depth loss L depth that aligns rendered depth D r with the aligned monocular depth D aligned (eq. (7)), a track loss L track that ensures consistent motion with the 2D trajectories, and the aforementioned ARAP, velocity, and acceleration losses for geometric regularization. In addition to [29], we introduce a novel motion mask loss that penalizes the rendered opacity of dynamic Gaussians at pixels identified as static by the motion masks

where P static t is the set of static pixels at timestamp t given by negating the motion mask M t , and O ( p ) is the accumulated opacity of dynamic Gaussians at pixel p (eq. (4)). This loss effectively prevents dynamic Gaussians from overflowing into static regions, maintaining a clean separation between static and dynamic components of the scene.

As more frames become available, we extend our dynamic reconstruction. For each new frame batch, we run CoTracker3 [20] in a temporal window of the new frame batch and an 8-frame overlap with previously processed frames to ensure continuity in the reconstruction. First, we identify 'recently visible' tracks as those marked as visible for at least 4 frames within the 8 overlapping frames and extend them into the new frame batch. These extended 2D tracks are then lifted into 3D using the latest camera pose and depth estimates. To identify newly visible dynamic regions, we back-project

all pixels within the fine motion masks of the new frames into 3D and perform a spherical search to determine whether each back-projected point has at least one lifted 3D track within a predefined radius r search. Pixels without nearby 3D tracks are marked as 'newly-seen', representing previously unobserved portions of dynamic objects. To capture these newly-seen regions, we execute a second run of the point tracker, specifically querying newly-seen pixels. Finally, we run a third tracking pass within the entire dynamic regions of the new frames to replenish the density of visible 2D tracks. This multi-stage tracking strategy ensures dense coverage of all dynamic elements in the scene.

Within the new temporal window, all extended and newly added 2D tracks are lifted into 3D using the same procedure as during initialization. For temporal consistency, we warp new tracks to past timestamps with DQB (Eqn.10), using Motion Scaffolds from the previous update. This leverages the latest photometric optimization to guide newly-seen 3D tracks through past frames despite invisibility. We initialize dynamic Gaussians only at newly-seen pixels, then jointly optimize geometry and appearance over the expanded MoSca and dynamic Gaussians using the same losses as during initialization.

4 Experiments

4.1 Experimental Setup

Implementation Details. All experiments were conducted on a cluster with an AMD EPYC 7H12 or 7742 CPU and an NVIDIA A6000 GPU. The kernel size of the median filter used to denoise the coarse motion masks is 5 × 5 . The spherical search radius for 'newly-seen' pixel identification is r search = 0 . 02 m . For geometry and photometric optimization, we keep our loss weights identical with those applied in MoSca [29], and set λ mask = 1 as the weight of L mask. For more implementation details, we refer to the supplemental material.

Datasets. We evaluate our camera tracking on the Bonn RGB-D Dynamic Dataset [47] and the TUM RGB-D Dataset [62] (dynamic scenes). Since existing works report tracking results on different sets of sequences, we select four mostly used sequences from each dataset to evaluate our method. For rendering, we report novel view synthesis (NVS) results both qualitatively and quantitatively on the iPhone dataset [12]. To align with Shape of Motion [68], we evaluate our method and all baselines on the 5 sequences used in [68] with the 2x downsampled image resolution. For a fair comparison, we use the preprocessed motion masks given by [68] for all our experiments on the iPhone Dataset.

Baselines. For tracking, we compare with various works on RGB and RGB-D SLAM. The main baseline is Splat-SLAM [54] since we base our tracking pipeline on it. For rendering, the baseline methods are NVS-capable monocular Gaussian-based dynamic reconstruction methods including Shape of Motion [68], DynOMo [56], MoSca [29] and Gaussian Marbles [61].

Metrics. For tracking, we evaluate ATE RMSE [ cm ] [63] after aligning the estimated camera trajectory with the ground truth via Umeyama alignment [67]. For NVS, we report PSNR, SSIM and LPIPS evaluated within the covisibility masks provided by [12] and averaged over all novel views.

4.2 Tracking

In tab. 1, we evaluate tracking performance on the Bonn RGB-D Dynamic Dataset [47] and the TUM RGB-D Dataset [62]. For both datasets, ProDyG performs competitively among all the RGB-D and RGB SLAM works and shows a significant advantage over the main baseline Splat-SLAM [54]. This improvement validates the effectiveness of our motion-agnostic camera tracking method introduced in sec. 3.1. WildGS-SLAM [89] is the only baseline method to outperform ProDyG on both datasets. Since both methods build upon the tracking framework of Splat-SLAM [54], we attribute this performance gap to two key factors: (1) WildGS-SLAM employs a test-time-optimized MLP that produces soft uncertainty masks to suppress confidence weights. As it reconstructs only the static background, it can aggressively suppress regions beyond actual dynamic object boundaries ( e.g. moving shadows) without degrading mapping quality. In contrast, ProDyG prioritizes accurate dynamic reconstruction and therefore requires motion masks with precise boundaries, which may miss some effective distractors. (2) WildGS-SLAM benefits from additional DINOv2 [46] features and on-the-fly training of the uncertainty MLP at the cost of higher computational complexity, while the primary computational overhead of our tracking approach comes from SAM2 [50] inference.

4.3 Rendering

In tab. 2, we evaluate NVS performance quantitatively on the iPhone Dataset [12]. Notably, we differentiate the experiment setups of each methods using the checkboxes, whether the reconstruction or tracking is executed online, and whether the input modalities are RGB only or RGB-D. As shown in tab. 2, ProDyG outperforms the offline method Shape of Motion [68] in both PSNR and SSIM, while running both the tracking and dynamic scene reconstruction online. This is a significantly more difficult task. Compared to MoSca [29], which is an offline method that shares the same representation of the motion field with our framework, ProDyG shows only minimal disadvantages when optimized with RGB-D input. This demonstrates the effectiveness of our progressive dynamic reconstruction. DynOMo [56] is the only method capable of online reconstruction with precomputed camera poses, and ProDyG shows a significant advantage over DynOMo when tested under the same constraint. Furthermore, ProDyG still maintains reasonably good performance when only having access to RGB images, while none of the baseline methods is capable of operating under the same setting. Finally, we evaluate NVS when estimated poses from the SLAM backend are loaded offline to the online mapper. The performance is very similar in both RGB-D and RGB-only modes, showing that our method can find the offline solution, even when optimized in pure online mode.

In fig. 4, we show qualitative comparisons of novel view renderings on the iPhone Dataset [12]. Compared to other methods, DynOMo [56] exhibits worse quality in novel view renderings due to lack of motion constraints ( e.g. 2D point tracking). Therefore, ProDyG is essentially the first method to support high-quality novel view synthesis of dynamic scenes through online reconstruction from monocular videos. Due to the motion mask loss (eq. (11)), dynamic objects reconstructed by our method tend to show more accurate silhouettes than Shape of Motion [68] and MoSca [29].

Limitations. Our dynamic representation struggles with objects that move predominantly outside the viewing frustum and later reappear, due to insufficient photometric constraints for Motion Scaffolds. This causes optimization to be dominated by regularization terms, often leading to undesired deformations. This issue is an inherent limitation also observed in MoSca [29]. Additionally, similar to most monocular dynamic view synthesis methods, our system requires per-scene test-time optimization, thereby limiting its applicability in real-time scenarios. Furthermore, ProDyG cannot effectively handle large changes in novel viewpoints, which would necessitate generative models or data-driven priors to hallucinate unseen regions. These challenges highlight key directions for future work.

paper-windmill

apple

DynOMo [56]

SoM [68]

ProDyG (Ours)

Ground Truth

5 Conclusion

Weproposed ProDyG, a progressive dynamic 3D reconstruction framework that meets four key criteria for practical deployment: online operation, global pose and map consistency, detailed appearance and geometry modeling through 3D Gaussian Splatting, and flexibility to operate with either RGB or RGB-D input. Our novel flow-based motion-mask prediction integrated with the SLAM backend enables robust camera tracking in dynamic environments, while our online dynamic reconstruction pipeline updates and optimizes Motion Scaffolds and dynamic Gaussians in a progressive manner. Our experiments demonstrate that ProDyG achieves competitive performance in both tracking and novel view synthesis.

Acknowledgments

We thank the Computer Vision Lab at ETH Zurich for providing the GPU compute resources used in this research, with approval from Ender Konukoglu. Erik Sandström conducted this work as part of his employment at Google Zurich, which supported his participation in this research; Google had no direct role in the project's design, execution, or analysis. Shi Chen acknowledges support from the NeurIPS 2025 Scholar Award and thanks Stefan Leutenegger for additional travel support.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe submission tackles online monocular dense dynamic scene reconstruction. The key differentiator to prior work is that depth input is optional. Similar to Motion Scaffolds, the method uses a number of pretrained models to extract depth, segmentation masks, and optical flow. It combines all this information with a motion scaffold to obtain a dynamic reconstruction. Unlike Motion Scaffolds, the proposed method works in an online setting. It borrows ideas like its keyframe handling and static reconstruction from Splat-SLAM to estimate camera poses online, which originally only handles static scenes.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nThe problem setting is very interesting and challenging. It is also timely, with two concurrent works [8,18], see Table 1 in the supplement and Table 1 in the main paper.\n\nThe Related Work section is very thorough and to the point.\n\nThe Method section is written well.\n\nThe method is designed quite well, and extends prior work in a sensible manner.\n\nThe authors promise a code release.\n\n\n\nWeaknesses:\n\nThe evaluation has very few qualitative results. There are no video comparisons to other methods.\n\nQualitative video results for Table 2 would be good to get a sense of how important depth input is and how well the method estimates poses when they are not given.\n\nOther than Table 2, there appear to be no ablations despite the rather involved design of the method. For example, does the motion mask algorithm need to be this complicated since it ultimately only seeds queries to SAM2 (and removes dynamic components for pose estimation but being over-eager there doesn't seem like a problem)?\n\nSomething seems off about the qualitative comparisons in Fig. 4: Why are the results of DynOMo so bad? The results on their project page look much better, including on the same scene.\n\nPresumably the tracking performance is evaluated on static and dynamic components? If so, the static components would heavily dominate the results, as seen by WildGS-SLAM (which apparently is only robust against dynamics and does not reconstruct them) dominating in Table 1. Please provide masked results where only the dynamic points are evaluated (even using coarse motion masks should give enough signal but preferably use the fine motion masks).\n\n*** Minor ***\n\nHow are poses in between keyframes estimated? Presumably, the method runs until it hits the next keyframe and then goes back and processes all non-keyframes in between in an online fashion? This could be clearer in the text.\n\nWhy is the method so slow, at 10-30 sec per frame? How is this split among the components? Presumably the pretrained models make up the bulk of that because I don't see how the presented method takes that much time otherwise?\n\nWhy is static Splat-SLAM treated as the main tracking baseline? It obviously does not handle dynamics.\n\nThe trajectories are hard to see in the video results.\n\nQUESTIONS:\nSee the major weaknesses above since they are intertwined. All major weaknesses are experimental. Although I enjoyed this submission, I'd like to see video results, ablations, and better tracking evaluations to recommend acceptance. Since the rebuttal format does not allow for videos, I am not sure that my concerns can be addressed in a rebuttal.\n\nLIMITATIONS:\nThe method is global for the static part of the scene but the dynamic part is only global \"accidentally\" by stitching together local dynamics, i.e. dynamic loop closure / track re-identification after prolonged invisibility is not tackled beyond accidental re-identification due to simple regularizers like ARAP. Please make this clearer in the Limitations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-14"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a complete online system that simultaneously tracks the camera and reconstructs both static backgrounds and dynamic foregrounds using 3D Gaussian Splatting (3DGS). The proposed pipeline leverages motion scaffolds to propagate 3DGS representations in real time and handles dynamic environments by integrating coarse and fine-grained motion cues.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nEffective Dynamic Masks:\nThe system combines DROID-SLAM for coarse dynamic masking and SAM2 for fine-grained motion segmentation. This combination is both practical and efficient, enabling robust foreground-background separation without requiring heavy supervision or extensive engineering.\n\nPracticality and Real-World Application:\nWhile the system is somewhat high-level and system-oriented, it targets real-world challenges in dynamic scene reconstruction. The integration of robust tracking and dynamic object modeling makes it highly applicable for robotics and AR/VR use cases. The effort to build a usable system in this underexplored direction is commendable.\n\n\nWeaknesses and Questions:\n\n1. Unclear Initialization Strategy:\nThe initialization phase of the system is not clearly described. It would be helpful to elaborate on how camera poses are recovered at the start and how the initial 3D reconstruction is built.\n2. Pose Estimation in Dynamic Scenes:\nIt's not fully explained how the system estimates accurate poses in the presence of dynamic elements. What is the robustness mechanism during scenes with large dynamic foreground motion?\n3. Optical Flow Redundancy:\nThe system uses DROID-SLAM for motion masking and CoTracker-3 for generating long-term dense 2D trajectories. A natural question arises: why not rely solely on CoTracker to distinguish dynamic and static regions? It could simplify the pipeline and reduce dependencies.\n4. System Redundancy and Efficiency:\nThe current design involves multiple overlapping components—DROID-SLAM for masks, SAM2 for refinement, and CoTracker for 2D tracking. While each tool contributes to robustness, the pipeline appears somewhat redundant. A discussion on potential simplification or trade-offs in performance vs. efficiency would strengthen the work.\n5. Real-Time Performance and Memory Profiling:\nThe paper claims real-time capability, but there is no detailed evaluation of runtime or memory usage across modules. A table or analysis comparing the runtime breakdown and GPU memory requirements of each stage would make the real-time claim more convincing.\n6. Pose and Motion Optimization Details:\nThe pose optimization process is not well explained. How does the system jointly recover camera poses and dynamic object trajectories? Is the optimization based on sparse or dense correspondences, and is it integrated into the 3DGS update?\n\nQUESTIONS:\nIt would be helpful if the authors could make the method section clearer and more organized.\nIt would also be great to include results on long video sequences.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis is a new online SLAM system that performs dense reconstruction of both static and dynamic scene elements, using gaussian splats. The emphasis is not so much on novel components as combining them in an intelligent and novel way into an end-to-end system with good performance.\n\nSTRENGTHS AND WEAKNESSES:\n[Strengths] The writing is excellent throughout, with clear and concise descriptions and a minimum of tedious puffery. This is a complex system with many moving parts and yet the paper manages both to describe it at a high level and to provide many specific details (with many more in the supplementary material). Reimplementing such a system is a major task but with the promise to release code, it should be possible to use it and base future research on it.\n\n[Strength] Both the overall approach and the many individual design decisions are well-motivated. To call out a couple in particular: I like the method of using flow to generate coarse masks and a segmentation model to refine them, and I was impressed to see that tracking and motion scaffolds have been added without breaking the online nature of the system.\n\n[Mixed] Evaluation is performed against a wide selection of baselines, and covers both tracking and novel view synthesis. However, the choice of datasets is somewhat limited, as are the metrics (no rotation error metrics are reported). Runtime numbers are also given (in the supplementary materials), though here there is not much comparison to other methods. (I was also curious to know whether the runtime might be competitive with other SLAM systems when used in a tracking-only manner.)\n\n[Weakness] There are not many qualitative examples, and no qualitative comparison of tracks. Are the top methods all similar enough that there is no interesting qualitative difference?\n\nQUESTIONS:\nMust field-of-view / intrinsics be supplied or does ProDyG optimize them (sorry if I missed this in the paper)? A column for this could be a nice addition to Table S1 (\"Method Classification\").\n\nl.101,113 describe threshold motion masks at 20%. Does this tell us something about how much dynamic content can be present before the method stops working successfully?\n\nThe MegaSaM system is claimed to also work well on scenes with low parallax (i.e. very little camera motion or motion with primarily rotation) that traditional SLAM systems tend to fail on. How does ProDyG fare on such cases?\n\nLIMITATIONS:\nYes, but it would also be informative (in the supplementary video) to see some qualitative examples of failure modes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
The paper proposes a method for online dynamic reconstruction from an unposed monocular video stream. The method works by computing masks for moving scene regions in two steps, and using dense bundle adjustment to compute keyframe poses based on the unmasked static scene regions. Interpolated poses for non-keyframes are then used along with scaled depth to generate the static regions of the scene as 3D Gaussians. The dynamic regions are reconstructed and updated using a previous method.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The task of online 4D reconstruction is novel and has huge potential in practical applications.
2. The paper is well-written, and provides a thorough and detailed overview of the method.
3. Given the constraints of online reconstruction, the results of the paper in comparison to past work (such as SoM) are impressive.

Weaknesses:
1. The method section is quite heavy on details and can become overwhelming. To avoid losing sight of the forest among the trees, I would suggest providing a more intuitive overview of each section, and better motivating the design decisions.

QUESTIONS:
1. Given that the overhead of optimizing dynamic Gaussians is likely to prevent real-time or even interactive execution, what is the particular use case the authors imagine for an offline method? And doesn't this limit the practicality of the method on the same lines as the previous approach mentioned in the introduction (L26-27)?

2. Since the dynamic reconstruction happens progressively, how does the temporal consistency of the reconstruction compare to the baseline methods? If space allows, it would be insightful to provide some evaluation (either qualitative or quantitative) of the temporal consistency of the proposed approach.

LIMITATIONS:
None
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-03