ARTDECO: Toward High-Fidelity On-the-Fly Reconstruction with Hierarchical Gaussian Structure and Feed-Forward Guidance
Authors: Guanghao Li
Kerui Ren
Linning Xu
Zhewen Zheng
Changjian Jiang
Xin Gao
Bo Dai
Jian Pu
Mulin Yu
Jiangmiao Pang
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Reconstruction
SLAM
Novel View Synthesis
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: ARTDECO unifies 3D foundation priors with structured scene representations, enabling robust and generalizable 3D reconstruction of diverse real-world scenes using only monocular video.

Abstract: On-the-fly 3D reconstruction from monocular image sequences is a long-standing challenge in computer vision, critical for applications such as real-to-sim, AR/VR, and robotics. Existing methods face a major tradeoff: per-scene optimization yields high fidelity but is computationally expensive, whereas feed-forward foundation models enable real-time inference but struggle with accuracy and robustness. In this work, we propose ARTDECO, a unified framework that combines the efficiency of feed-forward models with the reliability of SLAM-based pipelines. ARTDECO uses 3D foundation models for pose estimation and point prediction, coupled with a Gaussian decoder that transforms multi-scale features into structured 3D Gaussians. To sustain both fidelity and efficiency at scale, we design a hierarchical Gaussian representation with a LoD-aware rendering strategy, which improves rendering fidelity while reducing redundancy. Experiments on eight diverse indoor and outdoor benchmarks show that ARTDECO delivers interactive performance comparable to SLAM, robustness similar to feed-forward systems, and reconstruction quality close to per-scene optimization, providing a practical path toward on-the-fly digitization of real-world environments with both accurate geometry and high visual fidelity. Project page: https://city-super.github.io/artdeco/

Published as a conference paper at ICLR 2026
ARTDECO: TOWARDS EFFICIENT AND HIGH- FIDELITY ON-THE-FLY 3D RECONSTRUCTION WITH STRUCTURED SCENE REPRESENTATION
Guanghao Li Kerui Ren Linning Xu Zhewen Zheng 1.6
Changjian Jiang'. Xin Gao Bo Dai
Jian Pu Mulin Yu Jiangmiao Pang
"Shanghai Artificial Intelligence Laboratory. FFudan University, *Shanghai Innovation Institute,
*Shanghai Jiao Tong University, Thhe Chinese University Hong Kong, 'Carnegie Mellon University,
"Zhejiang University, The University of Hong Kong
(KITCHEN)
captuma
PSNRL 2866-J8 JB
KEEM 32,00 MBB
Figure 1: ARTDECO delivers high-fidelity interactive 3D reconstruction from monocular images, combining efficiency with robustness across indoor and outdoor scenes.
ABSTRACT
On-the-fly 3D reconstruction from monocular image sequences is long-standing challenge in computer vision, critical for applications such real-to-sim, AR/VR. and robotics. Existing methods face a major tradeoff: per-scene optimization yields high fidelity but is computationally expensive, whereas feed-forward foun- dation models enable real-time inference but struggle with accuracy and robust- ness. In this work, we propose ARTDECO, unified framework that com- bines the efficiency of feed-forward models with the reliability of SLAM-based pipelines. ARTDECO prediction, coupled with Gaussian decoder that transforms multi-scale features structured 3D Gaussians. To sustain both fidelity and efficiency at scale, we design hierarchical Gaussian representation with Lol-aware rendering strategy, which improves rendering fidelity while reducing redundancy. Exper- iments eight diverse indoor and outdoor benchmarks show that ARTDECO delivers interactive performance comparable to SLAM, robustness similar to feed- forward systems, and reconstruction quality close to per-scene optimization, pro- viding a practical path toward on-the-fly digitization of real-world environments with both accurate geometry and high visual fidelity. Project page: https://ciity suppergittub.io/rrddee
3D foundation models for pose estimation and point
Equual contribution Corresponding author.

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Published as a conference paper at ICLR 2026
INTRODUCTION
High-fidelity 3D reconstruction from monocular image sequences a long-standing goal in com- puter vision. Monocular data relatively inexpensive, ubiquitous, and easy to capture, while accurate 3D scene representations are crucial for downstream applications such as embodied in- telligence, AR/VR, or real-to-sim content creation. Recently, 3D Gaussian Splatting (Kerbl al.) 2023) has emerged as an efficient scene representation with strong empirical results. However, in monocular settings the lack of reliable geometric cues such as ambiguous scale, limited parallax, motion blur, and poor overlap makes it difficult to achieve accuracy, speed, and robustness at the same time, As a consequence, many existing systems optimize one objective at the expense of the others, limiting their practicality for online deployment.
Current 3DGS-basee 3D reconstruction methods fall into two paradigms. Per-scene optimization methods (Matsuki et al.] [2024] Huang et al., [024b] Meuleman et al., (025) rely on image poses estimated from Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM), achieving high accuracy but at the cost of substantial computation, with robustness limited by the fragility of these pipelines. In contrast, recent feed-forward models (Tang et al.) 2025 Yo et al.) 2025 Jiang et al. 2025b Zhang et al. 2025) leverage large-scale data to learn monocular priors and directly regress poses and Gaussian primitives with attention, enabling fast and robust infer- ence across diverse scenes but with limited rendering fidelity and weak consistency. This tradeoff highlights the need for approaches that combine the efficiency of feed-forward models with the strengths of per-scene optimization methods to deliver accurate, robust, real-time reconstruction. Beyond the efficiency-accuracy tradeoff, 3DGS pipelines are also highly sensitive to scene scale. As the scene grows, the number of Gaussian primitives required for training and rendering increases rapidly, which reduces efficiency. Prior attempts to address this either apply post-hoc anchor- based pruning, which lowers computation but introduces boundary artifacts and increases memory cost, or add multi-scale Gaussians during training, which mitigates artifacts but lacks explicit structural organization. These limitations underscore the need for a principled and practical level-of-detail (LoD) mechanism in 3DGS.
ARTDECO derives its name from a streamlined pipeline that unifies Accurate localization, Robust reconstruction, and Decoder-based rendering, with the aim of enabling on-the-fly 3D scene recon- struction and rendering. The core of ARTDECO is the goal of balancing real-time performance, accuracy, and robustness. It employs feed-forwatt models data priors to reduce monocular ambi- guities while maintaining the efficiency required for interactive use. To address the global inconsis- tency often seen in feed-forward approaches, ARTDECO integrates loop detection with lightweight bundle adjustment. Finally, a hierarchical semi-implicit Gaussian structure with LoD-aware densi- fication provides level-of-detail control through a spatially sparse grid attached to Gaussians, which jointly decodes scale and rotation attributes from both grid and Gaussian features. This enables the system scale effectively without excessive loss of fidelity or efficiency. Together, these compo- nents support practical real-time 3D reconstruction across diverse indoor and outdoor settings. Our main contributions can be summarized as follows:
We present ARTDECO, an integrated system that unifies localization, reconstruction, and ren- dering into a single pipeline, designed to operate robustly across various environments.
Notably, we incorporate feed-forward foundation models as modular components for pose es- timation, loop closure detection, and dense point prediction. This integration improves local- ization accuracy and mapping stability while preserving efficiency.
We further propose a hierarchical semi-implicit Gaussian representation with a LoD-aware densification strategy, enabling a principled trade-off between reconstruction fidelity and ren- dering efficiency, critical for large-scale, navigable environments.
Extensive indoor and outdoor experiments show that ARTDECO achieves SLAM-level effi- ciency, feed-forwant robustness, and near per-scene optimization quality, validating its effec- tiveness for practical on-the-fly 3D digitization.
Beyonnd the acronym, the name also evokes the Art Deco movement, valued for structure, geometry, and clarity of form. This metaphor reflects our system's emphasis on structured scene representations.
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RELATED WORK
2.1 MULTI-VIEW RECONSTRUCTION AND RENDERING
Neural Radiance Fields (NeRF) (Mildenhall et al., 2021 have attracted significant attention in novel view synthesis (NVS). NeRF and its variants (Barron et al. 2021; 2022 2023 Zhang et al., 2020); Verbin et al. 2022) model continuous volumetric fields and achieve high-quality image synthesis. However, the reliance on expensive volume rendering and large networks results in long training times and hinders real-time applications. To address these limitations, several works (Muller al. 2022 Xu et al.) [022] Sun et al. 2022) accelerate both training and rendering by introducing hybrid or explicit scene representations. Recently, 3D Gaussian Splatting (3DGS) (Kerbi et al. 2023) has shown remarkable progress in high-fidelity reconstruction and real-time rendering by representing scenes with anisotropic Gaussians and leveraging an efficient bile- rasterizer. Following its introduction, research has largely focused on model compression (Fan et al. 2024 Chen et al.
2025), large-scale scene processing (Ren et al, 2025 Jiang et al. 2025c Kerbl et al. 2024). and
geometry reconstruction (Huang et al. 2024a] Yu et al. 2024c][Géédon et al. 2025 Yu et 2024a).
Despite these advances in novel view synthesis (NVS), most methods assume access to accurate camera poses, typically estimated via Structure-from-Motiie (SfM) (Schonberger & Frahm 2016 Schönberger et al.] 2016) |an et al.] 2024), which imposes considerable preprocessing costs for large-scale or in- in-the-wild captures. To alleviate this reliance, several works (Lin et al. 2021 al.
2024 Lin et al. (2025) propose joint optimization of camera poses and scene parameters. However, these approaches remain computationally intensive, are sensitive to wide-baseline settings, or still depend on costly post-refinement.
2.2 STREAMING PER-SCENE RECONSTRUCTION
Classical visual SL.AM systems Mur-Artal & Tardós 2017 Engel et al. 2017 Teed et al. 2023 Li
cet al., [2255a] Chen et al. 2024 b) provide online tracking, mapping, and loop closure, serving as
the fundamental backbonn for wide range of computer vision Yann ct al. 2024b Gao & Pu 2025
Gao et al. 2025 Guo et al. 2025) and robotic tasks (Yan et al. 2025) However, these systems often rely on sparse or semi-dense geometric representations, which fall short in producing high-fidelity, photo-realistic maps required for immersive applications. To overcome this limitation, recent works integrate volumetric rendering techniques into SLAM to enable online NVS. Among these works, NeRF-based SLAM methods (Sucar et al., 202T Zhu al. 2022 Zhang et al.) 2023 Li al. 2026)
exhibit photorealistic reconstruction but remain computationally expensive due to per-ray volumetric rendering. By contrast, 3DGS has gained traction for SLAM integration thanks its explicit repre- sentation and efficient rendering. Utilizing the differential pipeline of 3DGS, some studies (Matsuki al.
2024 Hu et al., 2024, Keetha et al.] [024]] Yan et al.) [2024a] Deng al. 2024 Yu et al. Li ct al. 2025b Cheng et al. 2025a| Lin al.) (2025) directly propagate the gradient from the rendering loss to pose, while others ( (Yugay et al.] 2024|| Huang et al. 2024b Peng et al. 2024 Ha
2025
et al. 2024] Peng et al. 2025 Wen et al. 2025 Sandström et al. 2025) leverage traditional SLAM Modules to provide accurate pose. However, in monocular setting these systems often struggle to balance robustness, accuracy, and runtime efficiency. Recently, On-the-fly fly NVS (Meuleman et al. 2025) has shown that GPU-friendly mini-bundle adjustment with incremental 3DGS updates can enable interactive reconstruction, though robustness casual unposed videos remains limited.
2.3 FEED-FORWARD MODELS
Pretrained on large-scale datasets, recent feed-forward models (Wagg et al. 2025a 2024) recon- struct 3D scenes directly, avoiding per-scene optimization. These approaches can be divided into pose-aware and pose-free methods. Pose-aware models take images together with camera poses as input, enabling rapid reconstruction (Charatan et al.) [0244] [Cenn et al. 2024c] /Zang et al., 2024 Jiang et al. 2025a). Pose-free models, in contrast, perform fully end-to-end reconstruction from raw images alone, typically representing scenes with either point maps (Wang et al., 2024 Leroy et al. 2024 Wang et al., 2025a by Murai et al. 2025) or 3DGS (Jiang et al. 2025b). Notably, these feed-foree forward methods offer robustness across diverse scenarios, remove the need for preprocessing, and allow fast inference suitable for interactive use. However, they generally achieve lower accu- racy than per-scene optimized methods, and face challenges with maintaining global consistency, handling high-resolution inputs, and processing long sequences.
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Narting dant 1B Pll Stmani
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Figure 2: Frontend and backend modules. (a) Frontend: Images are captured from the scene and streamed into the frontend part. Each incoming frame is aligned with the latest keyframe using matching module to compute pixel correspondences. Based on the correspondence ratio and pixel displacement, the frame is classified as a keyframe, mapper frame, common frame. The lected frame, along with its pose and point cloud, is then passed to the back-end. (b) Backend: For each new keyframe, a loop-detection module evaluates its similarity with previous keyframes. If loop is detected, the most relevant candidates are refined and connected in the factor graph; other- wise, the keyframe is linked only to recent frames. Finally, global pose optimization is performed and other frames are adjusted accordingly. We instantiate the matching module with MASt3R (Leroy et 2024) and the loop-detection module with n3 (Wang al. 2025b).
with
3 METHOD
We aim to recover a high-fidelity static 3D scene together with the corresponding camera poses from
a monocular image sequence. Given sequence of monocular RGB frames [1,) :=)==11 with or without known camera intrinsics K E 3x33 we estimate the camera poses (R, t, ) associated with each image, as well as a set of Gaussian primitives { 1M that compactly represent the 3D scene. By default, we assume the scene is static and rigid, and that all geometric information is inferred purely from monocular cues without external sensors.
As illustrated in Fig. and 3 ARTDECO processes the sequence in streaming SLAM-style pipeline consisting of three modules: frontend, backend, and mapping. (1) The frontend estimates
relative poses and categorizes frames into common, mapping, keyframes (Sec. 3.1. (2) The
backend refines keyframe poses through loop closure and global bundle adjustment (Sec. B.2 (3)
Finally, image-wise pointmaps initialize 3D Gaussians, which are incrementally optimized in the mapping module (Sec. 3.3).
3.1 FRONTEND MODULE
For each input frame, the frontend estimates its pose relative to the latest keyframe and categorizes as common, mapping, or keyframe, We assume pinhole camera with fixed intrinsics and shared optical center. If the focal length is unknown, it is initialized from the first ky GeoCalib (Veicht al. 2024) estimates and jointly refined during pose estimation.
Pose Estimation. MASt3R (Leroy et al.) 2024) serves as our matching module, a two-view recon- struction and matching prior, to improve camera tracking and focal length estimation. Following MASt3R-SLAN -LAM (Murai et al. 2025). we obtain frame-wiss wise pointmaps, their confidence scores, and pixel correspondences between the current frame and the latest keyframe. The 3D points from the current frame are projected into the keyframe image plane, and the relative pose T E SIM(3) is estimated by minimizing reprojection residuals with a Gauss-Newton solver. Since MAS(3R pre- dictions are less stable near object boundaries, each point x, in the current frame, we estimate a local covariance Σg E R3x3 from neighbors within radius ö. We then project Σ. to the current keyframe's 's measurement space, which is used to filter out unreliable re- re-projection residuals. Besides, if the focal length is not provided, it is jointly optimized along with the relative pose. Further derivations given in A.2
weight residuals by per-point uncertainty. For
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Keyframe Selection. After pose estimation, each frame is categorized a common frame, mapper frame, or keyframe. A keyframe is created when the number of valid correponndenees with the latest keyframe falls below threshold TGe following standard SLAM practice. Keyframes passed to the backend for pose refinement and to the mapping module for reconstruction. A mapper frame is selected when the frame provides sufficient parallax for reliable multi-view reconstruction. We compute the pixel displacement between the current frame and the latest keyframe: if the 70th per- centile exceeds Tm. the frame is promoted a mapper frame. Mapper frames are first processed by the backend to compute pointmap confidence and are then used in the mapping module to initialize new 3D Gaussians. A common frame does not meet either the keyframe or mapper criteria and is therefore used only to refine existing scene details, without introducing new structure; its role will be further elaborated in later sections.
3.2 BACKEND
The backend processes keyframes from the frontend to maintain a globally consistent scene and camera trajectory. For each incoming keyframe, it evaluates correlations with carlier ones, builds a factor graph over the most relevant candidates, and performs global optimization to enforce multi- view consistency. In addition, it estimates the confidence of keyframe and mapper pointmaps, which are later used to initialize 3D Gaussian in the mapping module.
Loop Closure and Global Bundle Adjustment. Given new keyframe, the backend first updates the factor graph by connecting it to related frames, and then performs a PnP-based global bundle adjustment (BA) to refine poses, as illustrated in Fig. (b). If a loop closure is detected, the current keyframe is linked to its three most relevant predecessors; otherwise, is connected only to the latest keyframe. Loop detection is initially performed using the Aggregated Seleetive Match Kernel (ASMK). A loop is declared when a previous keyframe has an ASMK score above a threshold Лoop and is at least ktopp keyframes apart. To increase robustness against weak correspondences and noisy inputs, we further leverage the 3D foundation model n (Wang et al. 2025b) our loop-detection module. Specifically, the current frame and the top Na candidates from ASMK are processed by T to produce pointmaps, from which select the three most geometrically consistent keyframes based on angular error following (Murai et al. 2025). These are then connected to the factor graph, yielding more reliable loop closures and reducing drift. More details can be found in A.9
Pointmap Confidence. We estimate pointmap confidence using reprojection error rather than rely- ing on the confidence values predicted by MASt3R. When a mapper frame keyframe is processed, its pointmap is projected onto the N₂ previous keyframes with the highest ASMK scores. For each point, we compute the reprojection errors across the No keyframes, average them to obtain and define the confidence score as C = 1 if ē S Ec. and C = ã- otherwise, where E is prede- fined threshold. This reprojection-basee confidence provides a more reliable measure of geometric consistency across frames.
3.3 MAPPING MODULE
The mapping module reconstructs the 3D) Gaussian scene from incoming frames, their estimated poses, and pointmaps, as illustrated in Fig. 3 Unlike prior EDGGSbasee SLAM methods that rely only on keyframes, we leverage all frames to maximize the use of captured information: keyframes and mapper frames introduce new Gaussians, while common frames refine existing ones. This de- sign enriches both visual and geometric details, which are critical not only for accurate reconstruc- tion but also for high-fidelity rendering. Moreover, we introduce hierarchical Gaussian structure with LoD-aware control. LoD is essential for scalable scene modeling, particularly in large-scale, navigable spaces where SL.M-based applications require consistent detail at varying viewing dis- tances. By combining dense supervision from all frame types with principled level-of-detail man- agement, our mapping module improves fidelity of both reconstructed geometry and rendered views, while maintaining computational efficiency.
Probabilistic Selection for 3D Gaussian Insertion. When mapper frame or keyframe arrives from the backend, we determine where to initialize new 3D Gaussians. To avoid redundancy, Gaus- sians inserted only in regions that require refinement, rather than at every pixel, guided by image- level priors inspired by (Meuleman et al., (025). We prioritize high-freque frehufree regions and poorly reconstructed areas by computing an insertion probability at each pixel (u, u) using the Laplacian of
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Incoming hames
Ly, Lom
Brstand Rers
1003
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hut Wooe (nd. ind Ma joot
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Figure 3: Mapping process. When a keyframe or mapper frame arrives from the backend, new Gaussians are added to the scene. Multi-resolution inputs are analyzed with the Laplacian of Gaus- sian (LoG) operator to identify regions that require refinement, and new Gaussians are initialized at the corresponding monocular depth positions in the current view. Common frames not used to add Gaussians but contribute through gradient-based refinement. Each primitive stores position, spherical harmonies (SH), base scale, opacity, local feature, dmaxx and voxel index Vid- For ren- dering, the dmax attribute determines whether a Gaussian is included at a given viewing distance, enabling consistent level-of-detail control.
Gaussian (LoG) operator (Haralock Shapiro 1991
Pa (u, v) = max min * [(u,
v)ll vl),
min( I(u, w)ll 1),0,
(I)
where / and I are the ground-truth and rendered images, and G. is a Gaussian kernel with standard deviation A new Gaussian is added when Pa (u v) exceeds the threshold Ta-
Gaussian Primitive Initialization. After identifying candidate pixels, we initialize the correspond- ing 3D Gaussians. Each Gaussian is parameterized by its center μ4, spherical harmonics (SH), opacity a, base scale Sb. individual feature ft. and voxel index vtd. The μ and SHO are initialized from the pointmap and pixel color, while opacity is 0.2 C(a,s)
0.2-C(u.a) to down-weight low-confidence regions, where C(u,)) is the confidence score calculated in backend. Following (Meuleman et al., 2025 Wu
et al. 2025
Yu et al. 2024b), the base scale at pixel (u, v) is defined as:
d,s' S, f
S'
(2)
2 [l/mii[[[VVCC. Iuu, v),,)
where d; is the distance from the Gaussian center to the camera and f is the focal length. Here, s' represents an magee-paace scale, i.e., the expected distance to the nearest neighbor under local 2D Poisson process of intensity min[][(V * I(u, v)ll. 1), (Clark & Evans 1954). To ensure smoother reconstruction, we further refine scale and initialize rotation with two MLPs:
5 S, MLP, ④ St)
R = MLP, 4 St).
where + denotes concatenation, fi an individual feature initialized as zero, and fr is a region feature encoding local voxel context. We voxelize the 3D space with cell size when new Gaus- sian added to a voxel, the corresponding voxel-wise feature is initialized as zero and indexed by vaa. This hybrid region-individual design promotes global consistency of the Gaussian field while preserving local distinctiveness.
Levels of Detail Design. To support smooth navigation in large 3D scenes, we organize Gaussians into multiple levels of detail (LoD). Each Gaussian is assigned a level E N+ with / < L, where level denotes the finest resolution and level L 1 the coarsest, At initialization, Gaussian at level / corresponds to patch of 2 pixels in the original image (c.g., level O corresponds to one pixel). We progressively downsample the input frame I. times and initialize Gaussians from both the downsampled and original images. All Gaussian parameters follow the initialization described earlier, except that (i) the base scale weighted by 2 and (ii) each Gaussian is assigned distance- dependent parameter dmax D- where D is the distance from the Gaussian center to the camera. During rendering, a Gaussian is included if d, < dmaxx excluded if d, > 2dmaxx and smoothly faded out for dmax d, 2dmax by interpolating its opacity a' a represents the original opacity the Gaussians. This distance-awan aware LoD design suppresses flickering and maintains stable rendering quality across scales while preserving efficiency.
(2dmax dr.) /mmaxx where

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Training Strategy. To balance efficiency and reconstruction quality, we adopt staged training scheme. For streaming input, new Gaussians are initialized and the scene is optimized for K it- erations whenever mapper frame or keyframe arrives, while common frames trigger only K/2 iterations without inserting new Gaussians. Following (Meuleman et al. 2025 Wu et al. 2025). training frames are sampled with 0.2 probability from the current frame and 0.8 from past frames to mitigate local overfitting, After processing the sequence in a streaming manner, we run a global optimization over all frames, giving higher sampling probabilities those with fewer historical updates. Finally, camera poses are optimized jointly with Gaussian parameters, with gradients on positions and rotations propagated poses, consistent with common practice in on-the-fly recon- struction.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets and Metrics. We evaluate diverse indoor and outdoor benchmarks.
Indoor datasets
include 11 TUM scenes (Sturm et al. 2012), 14 scenes from ScanNet++ (Yeshwanth et al. (003)).
scenes from VR-NeRF (Xu et al. 2023), and scenes from ScanNet (Dai et al.
(017), with sequence lengths ranging from 32-5577 image frames. Outdoor datasets include 8 KITTI scenes (Geiger et al.]
2013). Waymo scenes (Sun et al. 2020) (both following S3PO-SLAM (Cheng et al. [2025a)), scenes from Fast-livo2 (Zheng et al, 2024), and scene from MatrixCity (Li et al. 2023). with lengths 200-1363 frames per trajectory. Reconstruction evaluated with PSNR, SSIM Wang al. 2004). and LPIPS (Zhang et al. 2018); pose accuracy with Absolute Trajectory Error (ATE) RMSE; and system efficiency with FPS.
Baselines. We evaluate against two categories of state-of-the--rt methods. For reconstruction qual- ity. we consider 3D Gaussian Splatting approaches, including OnTheFly-NVS (Meuleman et al, 2025)), ), LongSplat (Lin et al.) 22025). S3PO-GS (Cheng et al. 2025a). SEGS-SLAM (Wen et al.
2025 , MonoGS (Matsuki et al.] 2024)). For pose estimation, in addition to the aforementioned benchmark against several state-of fif-the-art art SLAM systems, includ- ing MAS33--LLAN (Murai et al. 2025), DPV-SLAM (Lipson et al.) 2024 DROID-SLAM TTed
3DGS-based SLA. methods,
& Deng 2021), and Go-SLAM (Zhang et al. 2023).
Implementation Details. Experiments are run on a desktop with an Intel Core i9- 14900K CPU and NVIDIA RTX 4090 GPU. Following standard Novel View Synthesis practices, every 8th frame is held out for evaluation: these frames are excluded from mapping but their poses are estimated and optimized for evaluation. In the frontend, we set 7 max(0.3333 W, 30), where W is the image width. In the backend, we use N₂ min(23, No) candidate keyframes, where N₂ is the number of available candidates. If Na < 11. loop- noo-dettction modules are disabled, and the top three ASMK- scoring keyframes are directly selected to connect in the factor graph. For pointmap confidence, we fix E. = 3. During mapping, 3D Gaussians are organized into 4 LOD levels by setting I
4.2 COMPARISON
Reconstruction Results Analysis. Tab. rreports reconstruction results on eight indoor and outdoor benchmarks. Our system achieves state-of-the-ar quality, particularly on challenging datasets such as TUM and ScanNet with structural complexity, motion blur, and noise. On higher-quality datasets like VR-NeRF and ScanNet++, where scenes feature diverse multi-scale visuals, all methods im- prove, yet ARTDECO still delivers the best performance. Outdoor evaluation covers large-scale free-motion captures (Fast-LIIV02) and fnward-facing driving datasets (Waymo, KITTI, Matrix- City). ARTDECO consistently outperforms baselines, demonstrating robustness to scale variation. Qualitative comparisons (Fig. 4 show that ARTDECO, enabled by its multi-level Gaussian primi- tive design, captures fine details, large-scale structures, and -ideeliyy geometry within a compact representation. Additional results are provided in Tabs. 8
28 Fig
in A.10
Tracking Results Analysis. Tab. 2 summarizes the tracking performance on indoor and outdoor benchmarks. With loop closure and covariance-mane matrix filtering, ARTDECO achieves markedly higher localization accuracy than other DDGS-based systems on challenging multi-scale indoor datasets (TUM, Sca On outdoor datasets such Waymo, it also delivers competitive

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SEGS SLAM
S3PO GS
OnThefly NVS
NVS
LongSplat
Our
GT
SR
KING
KING
USI.
KUNO
KING SE
KING
ICRL
Figure 4: Qualitative comparisons against popular on-the-fly fly reconstruction baselines across di- verse 3D scene datasets. ARTDECO consistently preserves high-quality rendering details in com- plex and diverse environments, particularly in the regions highlighted with colored rectangles.
Table 1:
Rendering comparisons against baselines across indoor and outdoor datasets. We report visual quality metrics, average running time.
Indoor-dataset
ScanNet++
ScanNet
TUM
VR-NeRF PSNR
Training
Method
PSNR SSIM LPIPS
PSNR SSIM LPIPS.
PSNR
SSTM
LPIPS.
SSIM LPTPS.
Timel.
MonoGS
16.71
0.682
0.600
18.87*
0.780* 0.629*
17.78
0.602
0.573
13.88
0.560
0.420
14.08
S3PO-GS
22.94
0.820
0.355
20.14
0.797
0.558
19.62
0.656
0.466
12.43
0.642
0.497
41.25 min
SEGS-SLAM
19.73'
0.839*
0.365
19.69*
0.743"
0.307
31.62"
0.896'
0.232"
10.84 min
ONYS
18.01
0.761
0.386
15.36
0.708
0.494
19.72
0.719
0.380
27.30
0.872
0.310
2.29 min
LongSplat
24.94
0.827*
0.260
19.27*
0.754
0.404
25.09
0.804
0.272
25.74*
0.832*
0.321
442.96 min
Ours
29.12
0.918
0.167
24.10
0.865
0.271
26.18
0.850
0,224
28.57
0.895
0.242
5.33 min
Outdoor-dataset
KITTI
Waymo
Fast-LIVO2 PSNR*
MatrixCity
Training
Method
PSNR1
SSIM; I.PIS..
PSNR1
SSIM I.PIS.
SSTM L.PPS.)
PSNR SSIM I.PISS.
Time
MonoGS
14.56
0.489
0.767
19.34
0.752
0.627
18.87
0.598
0.699
19.36
0.593
0.736
16.52 min
S3PO-GS
19.97
0.645
0.410
27.28
0.865
0.352
21.51
0.684
0.445
21.76
0.661
0.584
34.89 min
SEGS-SLAM
14.03
0.463
0.488
19.01*
0.698*
0.502*
24.58*
0.773*
0.307*
25.57
0.784
0.366
8.75 min
OnTheely-NNVS
16.89
0.579
0.471
25.53
0.820
0.360
18.76
0.618
0.497
21.36
0.687
0.451
0.74 min
LongSplat
16.86
0.532
0.447
25.61
0.795
0.326
26.37
0.792
0.276
313.60 min
Ours
23.17
0.765
0.299
28.75
0.880
0.276
29.54
0.894
0.158
25.62
0.790
0.327 6.58 min
majority scenes successful: -: majority failed; Only compare fully successful methods.
performance. Further results on TUM (Second part of Tab. demonstrate that ARTDECO consis- tently outperforms state-of-the-art non-3DGS SLAM methods, confirming its superior localization capability. Per-scene metrics, additional tracking results and qualitative trajectory comparisons provided in Tabs. 30 34 Figs.
A.10
Runtime Analysis. We compare runtime across IDGS-based methods both indoor and outdoor datasets (Tab. D. ARTDECO runs faster than all except OnTheFly-NVS, with its extra time cost primarily from pose estimation, trade-off justified by the superior pose accuracy in Tab.
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Table 2: Tracking comparisons. For tracking evaluation, we compare against SLAM- and SFM- based 3D reconstruction methods on indoor and outdoor datasets, as well as state-of-the-ar SLAM systems on the TUM dataset (Following -SSM, scenes from TUM frl). Our method consistently achieves lower ATE RMSE.
Dataset
MonoGS
S3PO-GS
SEGS-SLAM
MASI3--LAA
OnTheFly-NVS
LongSplat
Ours
ScanNet++
1.217
0.632
0.245
0.025
0.891
0.602
0.018
TUM
0.244
0.117
0.073"
0.031
0.025
Waymo
7.370
1.236
3.118
4.956
1.213
Metric
ORB-SLAM3
DPV+SLAM++ DROID-SLAM
Go-SLAM
MASI3R-SIA SLAM
Ours
ATE RMSE
0.054
0.038
0.035
0.030
0.028
#: majority of scenes successful; -: majority failed; Only compare fully successful methods.
Table 3: Quantitative results on ablation studies. We separately listed the rendering metrics and ATE RMSE on ScanNet++ dataset for each ablation described in Sec. 4.3
Frokt&-Backend
Full
w/ SLAM (MAS( x²)
w/ Loop (n²
vgg()
w/o loop
w/ dense key frame
ATE RMSE
0.018
0.374
0.096
0.057
0.094
Mapper
Full
w/o level-of-detail
w/o implicit structure
w/o global feat
w/o mapper frame
w/o common frame
PSNR
29.12
28.13
28.54
28.89
26.38
27.20
0.918
0.912
0.914
0.916
0.898
0.904
LPIPS
0.167
0.180
0.175
0.170
0.229
0.211
4.3
ABLATION STUDY
Ablation on Localization. We analyze the impact of backbone choice, loop closure, and frame categorization strategy on localization, as summarized in Tab. 3 We first ablate the feed-forward model used in the frontend and backend by replacing MASt3R (pairwise inference) with 3 (multi- image inference). Although 3 is trained on more diverse data, it lacks metric-scale capability and performs worse under varying viewpoints. In contrast, MASt3R better preserves consistent object proportions, resulting in more accurate pose estimation. Next, we ablate the loop-closure module by disabling which leads a significant degradation localization accuracy. Finally, we ablate the frame categorization strategy. Here, MF denotes mapper frames and KF denotes keyframes. Using both MFs and KFs (track w/ MF&KF) for inference provides additional temporal information, but unexpectedly reduces pose accuracy. This is because 3D foundation models often struggle with small marllpaaallaa inputs, producing ghosting and blur that corrupt point clouds and feature correspondences when the input sequence is overly dense.
Ablation on Reconstruction. We further ablate the effects of mapper frames, level-of-detan and structural Gaussians on reconstruction, as shown in Tab. 3 MFs add richer multi-view constraints, while LoD and structural Gaussians yield more compact, regularized representations, together im- proving reconstruction fidelity and rendering quality.
LIMITATIONS
While ARTDECO achieves strong reconstruction and localization, it has several limitations. First, it partly depends on feed-forward 3D foundation models for correspondence and geometry, which, despite enabling fast and scalable inference, reduce robustness under noise, blur, or lighting changes, and suffer when inputs fall outside the training distribution. Second, the system assumes consistent illumination and sufficient parallax; violations such as low-texture surfaces, repetitive structures, drift or artifacts. These challenges suggest future work
near-degenerate trajectories on incorporating uncertainty estimation, adaptive model selection, and stronger priors to improve generalization and reliability in real-world settings.

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Published as a conference paper at ICLR 2026
6 CONCLUSION
In this work, we present ARTDECO, unified framework that advances on-the-fly 3D reconstruction from monocular image sequences. Beyond achieving strong results on standard indoor and outdoor benchmarks, ARTDECO demonstrates that feed-forward priors and structured Gaussian represen- tations can be effectively combined within a single system to deliver both accuracy and efficiency. We see ARTDECO step toward practical large-scale deployment of real-to-sin pipelines, with promising applications in AR/VR, robotics, and digital twins.
7
ACKNOWLEDGMENTS
This work funded in part by the National Key R&D Program of China (2022ZD0160201) Shanghai Artificial Intelligence Laboratory, the National Natural Science Foundation of China (Grant No. 62502247) and the HKU Startup Fund.
ETHICS STATEMENT
This work focuses SLAM (Simultaneous Localization and Mapping) algorithms and does not involve human subjects, private or sensitive data, or applications that pose direct societal risks. All experiments are conducted on publicly available datasets commonly used in the community. We believe that our research raises no concerns regarding human subjects, privacy, fairness, bias, or potential harmful use.
REPRODUCIBILITY STATEMENT
We have taken steps to ensure the reproducibility of our results. All datasets used in our experiments publicly available benchmarks, as detailed in Section 4.1 and the Appendix. We provide com- prehensive descriptions of implementation details and experimental settings in these sections. Upon acceptance, we will release our source code and scripts to facilitate replication all experiments. Together, these measures ensure that our results can be independently verified and reproduced.
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THE USE OF LLM STATEMENT
We used GPT-5 as a general- general-purpose purpose assistant during the preparation this work. Specifically, GPT- 5 was employed for (1) refining the writing style of the manuscript improve clarity and readability, and (2) helping to organize and clean the code for presenting experimental results. GPT-5 was not involved in research ideation, algorithm design, in producing any novel technical contributions. All ideas, methods, and experiments in this paper are solely the work of the authors.
A SUPPLEMENTARY MATERIAL
organize the supplementary part as:
Sec.
A.1
describes Implementation & evaluation protocol.
Sec.
A.2
explains Jacobians and covariance transformation used in the Brontend/Backend.
Sec.
A.3
discusses additional experimental details, such as 7 ablations (multi-frame setup in
frontend/backend and observations).
Sec.
A.4
show an additional experiment with AnySplat miang et al. (025b).
Sec.
A.5
presents a comparison under low-texture and extreme motion scenarios to evaluate
robustness.
Sec. [.c presents more detailed ablation studies, including investigations into global_feat and Level of Detail (LOD).
Sec. A.7 presents multi-resolution experiment to demonstrate the antialiasing capabilities of our method.
Sec. A.8 details the Frontend implementation (correspondences, residuals/weights,, Gauss-
Newton updates, and focal optimization when intrinsics are unknown).
Sec.
A.9 details the Backend (loop-closure with ASMK and multi-frame 3D priors; global
bundle ad adjustment).
Sec. A.10 provides extended experiments, per-scene metries, and qualitative results.
A.1 MORE IMPLEMENTATION DETAILS
Comparison of Metrics. In previous methods, reconstruction metries often evaluated by moving the selected keyframes and evaluating the remaining frames. However, since each method selects different keyframes, the remaining frames vary, leading to slight differences in the images used to evaluate the Novel View Synthesis (NVS). For instance, selecting more keyframes results in fewer frames for evaluation, and vice versa. To ensure absolutely fair comparison, propose selecting one frame every eight frames for evaluation. The selected frames are not involved in the rendering process and are only used optimize their poses.
We have modified the code for all our baselines to ensure that their evaluation metrics are computed using this approach, where every eighth frame is selected for evaluation, and the evaluated frames are not involved in the reconstruction supervision but are only used for pose optimization.
A.2 JACOBIAN
Jacobian w.r.. Predicted Points. For completeness, we detail covariance transformation and related Jacobian in Sec. 3.1 Let m Y, 2) (f.X/Z+cz, G.Y/++c,, log Z) be the projection into image coordinates and log-depth with Jacobian Jx(x) p/xx The measurement-spe space covariance Σ. = Ja (x,a R&o Σ. J (xxx)" can derived, where R& the rotation matrix of the relative pose The between current frame and current keyframe. A residual is accepted only if det (E.)) < r. Here, det (Ecx) = II, > represents the generalized variance. The Jacobian is:
0
f.x
J (x(x))
f
7
2
0
1
(X,Y,2)=x;
This criterion rejects measurements with excessive uncertainty while remaining votation-invariane
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Jacobian w.r.t Focal. In Sec. 3.1 besides the Jacobian with respect the relative pose between the current frame and the keyframe, we also require the Jacobian of the projection with respect to the focal length f when it is not provided.
Given a pixel p = (w", vf) in the current frame, we first recover its camera- point P (X Y ) using the depth Z predicted by MASt3R. This point be re-projected into the current frame as
X y 2e
(uE - c (2")
p'c
(ue - cy)l ccy/s
where Ca, Gy) denote the intrinsic parameters with fax Ju =
Given the relative pose The between the current frame and a keyframe the transformed point is
X
p = The P
y
2
Finally, projecting p back to the image plane yields
x
z5
C2
u
T(P))
V
fez
+ Cy
log Z
log zt
Direct Effect of f: The direct effect refers to how f directly influences the projection of (X*, y*, y*, 2*) onto the image plane. This Jacobian term is straightforward and is given by the following matrix:
2
Auu v. log ?) 0f
yt
2
Indirect Effect of f: The indirect effect arises from how the depth-dependent point P (which depends f' affects the final transformed point p This term is computed by differentiating the reconstruction of p'c with respect to f:
(u-
0( )* 0f
0p's 0f 0f = (29)
The
(v--ey)
f
Full Jacobian: Finally, combining the direct and indirect effects, the total Jacobian J=,f is the sum of these two parts:
x
0
fx*
(u
(P*) = Ou, u, log Z) Ou,, v. log Z)
Ōpk
2t
ZF
12
f2
+
f
(v" -
05
OPk
0f
2
(2
fz
0
zF
A.3 ADDITIONAL EXPERIMENTS
In Sec. 4.3 replaced the MASI3R model, which is designed for two-frame inference, with the 3 model, which supports multi-frame inference. However, the ablation experiments show that the 3 model struggles to maintain consistent size ratios between the same objects across different viewpoints and disparities. this section, we provide a detailed description of the experimental setup and results for replacing MAS13R with the 3 model.
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Table Quantitative comparison between AnySplat Jiang et al. (2025b) and our method. We report Tracking Error (lower is better), PSNR, SSIM, and LPIPS across different view counts.
ScanNet++ (In-domain)
Fast-LiVO2 (Out-domain)
Method
Tracking
PSNR
SSIM
LPIPS
Tracking +
PSNR 1
SSIM f
LPIPS +
32 views
AnySplat Ours
0.082
23.41
0.822
0.176
0.235
24.21
0.772 0.853
0.198
0.034
26.53
0.856
0.201
0.065
27.96
0.187
100 views AnySplat Ours
0.128
21.37
0.780
0.227 0.218
0.426 0.270
21.28
0.672
0.287
0.076
24.36
0.841
24.56
0.788
0.255
Frontend with n² Inference and Keyframe Selection. In the frontend, we configure the n² model to infer & images once. As images are streamed, the frontend waits until ks images are accumulated before performing single inference. After inference, the Ray optimization method is applied to compute the correspondence between these frames, followed by local bundle adjustment (LBA) based on Sim(3) pose optimization among the k frames. Upon completing this process, we follow the keyframe selection strategy described in Sec. 3.1 and the selected keyframes are then passed to the backend.
After the first inference, for each subsequent inference, the frontend first selects the most recent keyframes and waits for the next k - frames to complete the batch of k frames. Then, another inference and LBA are performed, and the keyframe selection is updated accordingly.
Backend with 3 Inference and Global Bundle Adjustment. The backend receives the selected keyframes from the frontend. Similar to the frontend strategy, the backend waits until keyframes accumulated before performing T² inference. Once enough keyframes available, correspon- dence is computed between them, followed by a Global Bundle Adjustment (GBA) to optimize the poses.
In each inference step, the backend also includes historical keyframes into the inference window, ensuring sufficient overlap between keyframes from multiple inference steps. Unlike the frontend, however, the backend leverages the streamable ASMK algorithm from the MASt3R-SLAM frame- work compute the most similar historical keyframes to the current keyframe. These selected keyframes are then included in the inference window to perform the optimization.
Mapper with Keyframe Reception. We did not modify the mapper code. The mapper simply receives the keyframes sent by the backend and proceeds with the reconstruction following the process described in Sec. 3.3
A.4 COMPARISON WITH FEED-FORWARD METHODS
As summarized in Table we evaluate our system against state-of-the-art feed-forward approaches, specifically AnySplat. While feed-forward methods offer a simplified pipeline, they suffer from sev- eral inherent bottlenecks: restricted operating resolutions, prohibitive memory growth on extended sequences, and the absence of backend optimization module to rectify accumulated pose drift.
Our quantitative results on ScanNet++ Yeshwanth et al. (2023) (in-domain) and Fast-LiVO2 Zheng et al. (2024) (out-domain) highlight these deficiencies. Notably, as the sequence length increases from 32 to 100 views, AnySplat exhibits a significant performance degradation; for instance, its tracking етгоr on ScanNet++ rises from 0.082 to 0.128, and PSNR drops by nearly dB. In con- trast, our modular SLAM-style design maintains superior stability and reconstruction fidelity. The consistent gains across both datasets, especially the robust performance the out-domain Fast- LiVO2, underscore the necessity of systematic post-optimization for reliable long long-sequence SLAM and high-quality radiance field synthesis.
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Table Quantitative evaluation of tracking accuracy (ATE RMSE [m] )) and reconstruction quality (PSNR 1, SSIM 1. LPIPS J). The symbol "/" indicates system crash, tracking failure, or the lack of rendering capability for that specific baseline.
TUM (Low Texture)
TartanAirV2 (Extreme Motion)
Method
ATE
PSNR
SSIM
LPIPS +
ATE +
PSNR
SSIM
LPIPS
+
LDSO
0.207
0.230
ORB-SLAM3
/
Go-SLAM
0.371
/
0.258
DROID-SLAM
0.407
0.301
DPV-SLAM++
0.054
0.223
MASGR-SLAM
0.076
0.537
NICER-SLAN SLAM
/
1,666
12,43
0.445
0.671
MonoGS
0.671
27.43
0.912
0.353
/ 0.631
S3PO-GS
0.524
28.97
0.916
0,340
1.260
14.87
0,598
-LGS-SLAM
/
OnTheely-NVS
0.565
26.62
0.918
0.355
1.116
15.34
0,635
0,527
LongSplat
0.231
31.68
0.911
0.296
1.244
17.26
0.639
0.500
Ours
0.072
33.74
0.950
0.264
0.095
23.17
0.813
0.268
A.5 ROBUSTNESS EVALUATION
To verify the robustness and versatility of our system, we conduct extensive evaluations against broad spectrum of baselines. These include classic SLAM frameworks such as BRB-SLAME Cam-
pos et al. (2021). DROID-SLAM TTed & Deng
2021 Go-SLAM Zhang et al. (2023). DPV-
SLAM+ Lipson al. (2024). L.SSO Gao et al. (2088), and the recent MAST3R SL.AM Murai
al. (2025). We further compare against statc-of- thrrr-rrtttttttt 3DGS-based SLAM methods, including MonoGS Feng et al. (2025), S3PO-GS Cheng et al. (2025b), and SEGS-SLAM Wen et al. (0255,, as
well advanced reconstruction approaches like On OnTTheFy-NVV Meuleman al. (2025) NICER-
SLAM Zhu et al. (2022), LangSpla([Lin et al. (2025).
Our evaluation focuses two challenging scenarios: the TUM RGB-D Structure Texture cat- egory to assess performance low-texture environments, and the TartanAirV2 Hard sequences to test tracking and mapping stability under extreme camera motion. As shown in Table many isting methods suffer from tracking failures or system crashes (denoted by /) in these demanding conditions. In contrast, our method achieves the lowest ATE RMSE both datasets, demonstrating superior localization stability.
Furthermore, the reconstruction quality results in Table highlight our system's 's ability main- tain figh-fiieeitty rendering even when geometric cues are sparse motion blur is prevalent. Our approach significantly outperforms both traditional and learning-basee baselines across all metrics (PSNR, SSIM, and LPIPS), establishing its effectiveness for robust spatial Al applications.
A.6 ADDITIONAL. ABLATION STUDIES
To further validate the contribution of each core component in our system, we provide a comprehen- sive set of ablation experiments. These studies focus the impact of mapper frames, Level-of- Detail (LoD) strategy, and the structural design of Gaussians.
Removing mapper frames forces the Gaussian initialization process to rely solely on keyframes. which reduces the number of initialization frames by 50-70%. As shown in Table ( this leads to overly sparse geometry and significant drop in reconstruction quality (2.74 dB PSNR), confirming that mapper frames are essential for maintaining density and stability in online mapping.
Impact Mapper Frames.
Level-of-Detail (LoD) and Gaussian Selection. Unlike prior methods like Octree-GS that assume a static, complete point cloud, our system is designed for incremental growth. By introducing a pixel-wise, point-cloud-wware selection attribute, achieve significant reduction in the number of
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Table Consolidated ablation results, #Render denotes the number of rendered Gaussians thou- sands (K), and MEM denotes the GPU memory usage in MB. The best results are highlighted in bold.
Method
PSNR
SSIM
LPIPS 4
#Render (K)
MEM (MB)
Ours (Full)
29.12
0.918
0.167
127.2
(1) Mapping Strategy
Ours w/o mapper frames
26.38
0.898
0.229
73
78.8
(2) LoD Selection Mechanism
Ours w/o gs selection
29.12
0.918
0.167
176
127.2
Ours w/o level-of detail
28.13
0.912
0.180
181
133.3
(3) Structural Feature Ablation (wlo LoD)
w/o global feat
27.95
0,910
0.197
161.5
global feat (8) +
feat (24)
28.11
0,912
0.181
141.2
global_feat (16) + local_feat (16)
28.13
0,912
0.180
181
133.3
global.feal (24) + loca (8)
28.03
0.911
0.185
182
127.6
w/o local_feat feat
27.98
0.910
0.191
183
121.7
rendered Gaussians (from 176K 130K) without sacrificing fidelity. Without the LoD mechanism, the system suffers from increased computational overhead and degraded high- frequerr details.
Structural Gaussian Design. We analyze the effectiveness of decoding Gaussian attributes from a combination of global features and local features. Our experiments indicate that a balanced fea- ture distribution (e.g.. 16-dimensional global and 16-dimensional local features) provides the best trade-off between reconstruction accuracy (PSNR/LPIPS) and memory efficiency. This structured approach enforces local coherence and accelerates convergence compared to unstructured baselines.
A.7 ANTI-ALIASING EXPERIMENT
To evaluate the efficacy our anti-aliasing selection strategy, we conduct a multi-resolution abla- tion study on the "Bicycle" scene from the Mip-NeRF 360 dataset [Brron et al. (2022). adhering to the experimental configuration established by Octree-GS Ren et al. (2025). Our method intro- duces scale-aware filtering mechanism based on modified distance metric d,e s denotes the scale factor determined by the ratio the standard resolution to the current render- ing resolution. During the rendering process, Gaussian primitive included if d,s d,max and excluded if drs 2 2 dmaa- As summarized in Table and illustrated Figure 10 of the supple- mentary material, this strategy significantly enhances rendering efficiency by drastically reducing the Gaussian count at lower resolutions. For instance, at L/8 resolution, the number of rendered primitives pruned from 1131K mere 29K while maintaining comparable PSNR and SSIM metrics. Furthermore, the anti-aliasing module effectively preserves structural integrity across vary- ing scales; without this mechanism, the bicycle frame appears disproportionately thick low reso- lutions, while fine structures like spokes become excessively thin at high resolutions. By selectively rendering only scale-appropriate Gaussians, our approach ensures consistent geometric proportions and sighhiiiyiity reconstruction across all resolution levels.
dr, where
Table 7: Multi-resolutie Ablation Study on the Mip-NeRF 360 Barron et al. 2022 "Bicycle" Scene. The results demonstrate that our anti-aliasing strategy significantly reduces the number of rendered Gaussians (#Render) at lower scales while maintaining robust reconstruction quality across four resolution levels (1x x to 1/8x).
Scale
1/2x
1/4x
1/8x
Method
PSNR
SSIM
#Render
PSNR
SSIM
#Render
PSNR
SSIM
#Render
PSNR
SSIM
#Render
w/o anti-alias
20.27
0.397
1083K
21.67
0.475
1083K
23.60)
0.614
1097K
25.01
0.743
1131K
Ours
20.48
0.417
1008K
21.83
0.491
657K
23.62
0.621
167K
24.61
0.732
29K
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A.8 DETAILS IN Frontend
For completeness, we also provide the details for pose/focal optimization in the frontend. Inspired by MASC3--LAM Murai et al. (2025), we use MASt3R Leroy al. (2024) estimate dense pixel-wise correspondences and associated 3D points between the current frame and the current keyframe. We denote the set as C (P
P Pe 2713 = x
P
Y m= where p P E TR² are pixel m. 7 E IR3 are MASt3R-inferred 3D
J
coordinates and P = [X mi m. Z E points expressed in the current-frame and keyframe coordinate systems, respectively. Given C, we project the current-frame 3D points into the keyframe image plane and form reprojection residuals, which are minimized using Gauss-Newton
P
Newton procedure. We estimate the similarity transform Tke = (Ske: Rkc, tkk) E Sim(3) with 0, Rice SO(3), tke R3; when intrinsics are unknown, additionally optimize single focal length f.
For each correspondence, compose the similarity transform and projection directly into the per- point residual:
y Pm (
P = Skc R +t, TK(S)(
(5)
log
log( 21h
where K(f) is the camera intrinsic and TK(S) is the pinhole projection function.
To encode measurement confidence and balance pixel vs, depth scales, each correspondence m is assigned positive semidefinite weight matrix W., E B3x3 The (Mahalanobis) squared residual
m
I' O,
Vm is the residual norm.
(6)
We use the robust Huber kernel to eliminate the influence of outliers.
1,
8m
S 6,
Wm
8
W m Wmo Wms
(7)
8m
8,
Sy
+
with small > 0 for numerical stability.
With robust weights, the objective becomes a weighted least-squares problem:
M
Erob
m
W
Im
(8)
2m1
Linearizing Im at the current estimate yields the normal equations
3rm 00
Σ J.W mJ A0 Σ J W m Ims
J
E R3xd
(9)
where = for pose-only 0 = [Tkc} and = the focal f is also optimized. We update the similarity transform by the Sim exponential map The +- expsim(3) (Aésim) Tec. and, when applicable, the focal by -f Af. Here A E g² is the minimal Sim(3) increment and AO A As] if f is included.
A.9 DETAILS IN Backend
Details about Loop Closure. For the current keyframe received from the backend, we first com- pute the similarity score between the current keyframe and historical keyframes using the ASMK algorithm. Only historical keyframes with similarity score greater than 0.005 are considered as valid matches. However, the ASMK-baser loop closure detection algorithm, relying solely on 2D features, lacks 3D prior knowledge and is not well-suited robust loop closure metric. Recently, multi-frame 3D foundation models, such as VGGT, 3², have provided a new approach for loop closure detection by incorporating rich 3D priors.
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Based on this idea, we first calculate the maximum time gap between the current keyframe and the valid historical keyframes. If the maximum time gap exceeds 10, we assume that the current keyframe has a high probability of being similar to the historical keyframes. In such cases, revisit the previously computed ASMK score and select the top N₂ most similar keyframes. These keyframes then used for T inference alongside the current keyframe.
As long as there is overlap between the keyframes (even not), 3D foundation models can leverage the rich 3D priors infer point clouds in the same coordinate system. We then calculate the angu- lar error between corresponding points in different frames. If the angular error is below a certain threshold and the distance between the points is also small, we consider them as match. After calculating the ratio of matched pixels between the current frame and historical keyframes to the total number of pixels, we sort the matches by their ratio. We identify the top three frames where the ratio exceeds 0.15 and consider these as loop closures.
In an ideal scenario, every incoming keyframe would be inferred with all historical keyframes using T inference. While this approach would significantly enhance the loop closure ability, it is com- putationally expensive and time-consuming. Therefore, we opted for the aforementioned approach based on ASMK pre-filtering as compromise. This method effectively balances both accuracy and speed, as demonstrated by our experiments, making it more practical for real-time applications.
Details about Global Bundle Adjustment. For completeness,
also provide the details for global bundle adjustment. For a correspondence m between frames i and j. let pi E IR be the 3D point in frame-i and pl IR² the observed pixel in frame-j with log-depth e The world poses are T = R,, t,) Sim(3) and Twj ($j. R2,t,) E Sim(3). The world point and its coordinates in frame-j j are
p m
8; R. P + t.:
pi m - sj R. (P t2):
with (8) Yi. 2 T
Pj Using intrinsies K, the projection is
p k (P% pm) [fo 2/ + Cz. fuYin/ 2r, tc]
and we stack the plane- and log-depth terms into
P/n P/v log 2%, en
I'm
E R..
(10)
The Jacobian of rm with respect P/, is
fo
82
2
(2h
Örm m
0
fu
8,8
(11)
aP m
22.
12
0
22
To optimize the poses, we apply left-multpliccttive Sim(3) perturbations ,E,, RR7 to T Tii and Twy. respectively. With the skew operator [-]x and P defined above, the induced differentials of the point in frame-j are
OPj
5j
R
[
P
Pw m_ e R3x7
dgg.
(12)
OP
[-
[P
p
Rx7
x
which combine with the projection Jacobian via the chain rule:
Drm drm OP! drm Orm OP 0oE,
(13)
08E, aP. 06g,
ap. m 06E,
Finally, to initialize reliable 3D supervision for mapping after the pose update, for the current N2 keyframe l with points (X n and related keyframes Ng. we project each point to every No.
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compute the mean reprojection enror ent assign confidence On by thresholding, and pass the pair to the mapping thread:
1
luu
TK(TTb xx)
[W
JEN
(14)
En S Tproj
Cn
On Tpouj:
Cn
Fproj
yielding the package (X* Cn ) for Gaussian-primitive supervision in the mapping thread.
A.10 MORE EXPERIMENTS
In this section, we provide the specific per-scene metrics for each dataset used to support the average metries reported in the main text through Tab. 8 34 These include reconstruction metries such as PSNR, SSIM, and LPIPS for the following datasets: KITTI, Waymo, MatrixCity, Fast-LIIVO2, Scan- Net, ScanNet++,, VR-NeRF, and TUM. We also provide qualitative results related to reconstruction
and tracking trajectories, shown in Fig 6
Table 8: PSNR the Fast-LIVO2 Dataset
Method
B.D_Builingg.01
HKUpCsampus
Red Reu.Sculpture
Retail.Street
SYSU
MonoGS
19.86
21.70
15.50
18.05
19.23
SEGS-SLAM
26.26
29.55
18.49
24.01
S3PO-GS
17.47
25.47
18.89
24.30
21.42
OnTheFly-NVS
17.79
21.46
17.50
17.67
19.39
LongSplat
29.25
29.45
24.97
23.10
25.07
Ours
31.11
30.89
26.29
29.20
30.22
Table 9: SSIM on the Fast-LIVO2 Dataset
Method
CBD Building.O0 01
HKU. UKKU_Campus
Red Sculpture
Rtreet Street
SYSU
MonoGS
0.698
0.608
0.518
0.554
0,610
SEGS-SLAM
0.880
0.847
0.589
0.777
S3PO-GS
0.645
0.705
0.620
0.782
0.669
OnTheFly-NVS
0.677
0.628
0.590
0.558
0.635
LougSplat
0.891
0.832
0.786
0.708
0.742
Ours
0.940
0.871
0.861
0.901
0.899
Table 10: LPIPS the Fast-LIVO2 Dataset
Method
BBDDBuilding.01
HKU_Campus
Red_Sculpture
Retail_Streed
SYSU
MonoGS
0.623
0.687
0.778
0.750
0.658
SEGS-SLAM
0.213
0.205
0.516
0,292
S3PO-GS
0.592
0.448
0.505
0.232
0.451
OnTheFly-NVS
0.490
0.470
0.500
0.528
0.498
LongSplat
0.179
0.258
0.304
0.315
0.322
Ours
0.108
0.199
0.205
0.127
0.151
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Table 11: PSNR on the TUM Dataset
Method
f1.360 fl desk fl desk2
fl floor f1 plant fl room fl rpy fl teddy
fl xyz
f2 xyz f3 office
MonoGS
16.17
14.86
14.96
20.71
17.46
15.38
16.28
16.50
21.89
21.23
20.10
SEGS-SLAM
19.43
19.81
18.44
21.75
17.33
18.44
15.37
20.68
19.54
26.14
S3PO-GS
16.70
20.09
18.52
22.69
18.41
17.14
16.67
19.02
22.78
23.06
20.74
OnTS
18.44
18.91
18.38
25.43
15.84
17.26
20.86
16.27
25.69
20.04
19.80
LongSplat
27.07
25.35
25.48
28.14
21.27
23.52
22.81
22.36
26.75
27.46
25.80
Ours
26.19
26.04
25.54
29.43
24.06
25.23
24.92
23.30
26.50
29.91
26.92
Table 12: SSIM on the TUM Dataset
Method
f1_360 fl _l_desk fl_desk2 fl fffoorr fl fl_plant fl_romm fl_rpy fl _eedy fl_xyz f2.xyz f3_office
MonoGS
0.583
0.529
0.552
0.586
0.581
0.542
0.575
0.539
0.738
0.698
0.698
SEGS-SLAN SLAM
0.751
0.775
0.720
0.752
0.641
0.718
0.622
0.821
0.769
0.861
S3PO-GS
0.602
0.680
0.650
0.625
0.616
0.597
0.596
0.614
0.762
0.752
0.723
OnTheFly-NVs
0,725
0.712
0.713
0.783
0.600
0.662
0.755
0.594
0.870
0.730
0.760
LongSplat
0.826
0.833
0.842
0.767
0.695
0.790
0.783
0.736
0.888
0.879
0.799
Ours
0.850
0.861
0.859
0.838
0.797
0.838
0.847
0.768
0.883
0.922
0.882
Table 13: LPIPS on the TUM Dataset
Method
[1.360 [I desk fl desk2
fl floor fl plant fl room f'l rpy fl teddy fl xyz 12,xyz B office
MonoGS
0.642
0.664
0.661
0.736
0.572
0.679
0.509
0.654
0.326
0.355
0.511
SHGS-SIAM
0,361
0.244
0.358
0.277
0.399
0.336
0.415
0.205
0.270
0.200
S3PO-GS
0.551
0.433
0.506
0.634
0.461
0.571
0.507
0.482
0.296
0.270
0.420
OnTheFly-NVS
0.445
0.396
0.404
0.305
0.501
0.440
0.353
0.505
0.199
0.326
0.307
LongSplat
0.324
0.266
0.270
0.305
0.406
0.255
0.267
0.300
0.127
0.152
0.325
Ours
0.279
0.220
0.238
0.233
0.263
0.251
0.235
0.298
0.174
0.080
0.191
Table 14: PSNR on the ScanNet Dataset
Method
scene0000.00
scene0059_00
scene0106.00
scene0169
scene0181_00
scene0207_og
MonoGS
18.09
18.03
19.71
19.37
19.16
SEGS-SLAM
17.92
20.89
21.40
18.69
S3PO-GS
17.98
19.54
20.27
21.24
21.20
20.62
OnTheFly-NVS
14.88
16.33
14.66
16.37
15.47
14.47
LongSplat
19.52
19.01
19.71
18.94
19.17
Ours
23.28
24.74
26.34
23.07
21.80
25.37
24

---

Published as a conference paper at ICLR 2026
Table 15: SSIM on the ScanNet Dataset
Method
scene0000.00
scene0059_00
scene0106_00
scene0169_00
scene0181_00
scene0207_00
MonoGS
0.733
0.774
0.792
0.823
0.776
SEGS-SLAM
0.804
0.850
0.902
0.799
S3PO-GS
0.738
0.769
0.816
0.815
0.845
0.797
OnTheFly-NVS
0.742
0.747
0.667
0.721
0.667
0.705
LongSplat
0.752
0.764
0.750
0.782
0.721
Ours
0.824
0.863
0.905
0.859
0.889
0.848
Table 16: LPIPS on the ScanNet Dataset
Method
scene0000 00
scene0059.0g 00
scene0106.00
scene0169 .00
scene0181 11.001881000
scene0207 .00
MonoGS
0.713
0.597
0.612
0.578
0.644
SEGS-SLAM
0.405
0.352
0.269
0.432
S3PO-GS
0.700
0.518
0.484
0.550
0.513
0.583
OnTheFly-NVS
0.464
0.477
0.554
0.504
0.491
0.474
LongSplat
0.402
0.384
0.384
0.423
0.427
Ours
0.254
0.279
0.237
0.278
0.288
0.290
Table 17: PSNR the Waymo Dataset
Method
100613
106762
132384
13476
152706
153495
158686
163453
405841
MonoGS
20.05
20.91
22.71
19.51
21.23
14.15
20.29
19.01
16.19
SEGS-SLAM
20.14
23.60
22.52
24.11
21.16
21.22
19.01
S3PO-GS
25.36
28.23
27.01
24.85
28.53
26.54
26.03
23.65
27.28
OnTheFly-NVS
26.95
27.21
25.31
24.34
25.45
26.41
26.30
23.97
23.79
LpnaSplat
24.25
23.70
24.11
24.42
25.66
23.74
24.84
22.69
25.61
Ours
27.98
30.84
30.32
28.03
29.80
27.60
26.83
26.91
30.48
Table 18: SSIM on the Waymo Dataset
Method
100613
106762
132384
13476
152706
153495
158686
163453
40584
MonoGS
0.758
0.816
0.855
0.713
0.792
0.672
0.723
0.745
0.695
SEGS-SLAM
0.730
0.805
0.824
0.784
0.734
0.677
0.698
S3PO-GS
0.828
0.878
0.883
0.778
0.856
0.846
0.819
0.797
0.865
OnTheFly-NVS
0.850
0.854
0.864
0.764
0.795
0.847
0.837
0.785
0.788
LongSplat
0.773
0.766
0.822
0.716
0.778
0.755
0.777
0.732
0.795
Ours
0.865
0.906
0.919
0.856
0.882
0.871
0.847
0.866
0.907
25

---

Published as a conference paper at ICLR 2026
Table 19: LPIPS the Waymo Dataset
Method
100613
106762
132384
13476
152706
153495
158686
163453
405841
MonoGS
0.610
0.534
0.451
0.745
0.666
0.710
0.627
0.664
0.633
SEGS-SLAM
0.484
0.399
0.384
0,450
0,472
0.495
0.502
S3PO-GS
0.329
0.276
0.275
0.471
0.427
0.379
0.373
0,411
0.352
OnTheFly-NVS
0.328
0.337
0.361
0.378
0,401
0.348
0,307
0.376
0,400
LongSplat
0.356
0.328
0.355
0.354
0.397
0,430
0.321
0.371
0.326
Ours
0.308
0.237
0.265
0.267
0.304
0.313
0.283
0.289
0.216
Table 20: PSNR the VR-NeRF Dataset
Method
appartment222 kitchen261 kitchen262 kitchen263 table6l workspace61 workspace62 workspace664
MonoGS
18.43
16.91
11.66
14.47
15.50
14.80
15.12
14.80
SEGS-SLAM
26.14
31,81
32.65
36.55
30.95
S3PO-GS
28.45
27.98
25.16
18.56
19.36
22.16
23.49
22.17
LongSplat
31.22
27.10
27.76
24.04
24.90
22.32
22.84
OnTheFly-NVS
30.52
30.24
27.51
25.16
27.77
22.08
26.04
29.05
Ours
32.98
30.90
30.23
29.04
29.05
24.68
24.63
27.13
Table 21: SSIM on the VR-NeRF Dataset
Method
uppartment262 kitchen261 kitchen262 kitchen263 table61 workspace61 workspace662 workspace6i4
MonoGS
0.646
0.627
0.506
0.599
0.595
0.526
0.581
0.522
SEGS- SLGS
0.831
0.910
0.883
0,949
0.905
S3PO-GS
0.875
0.880
0.856
0.719
0.737
0.762
0.791
0.758
LongSplat
0.905
0.861
0.888
0.803
0.823
0.757
0.787
OnTheFly-NVS
0.912
0.903
0.900
0.847
0.882
0.775
0.855
0.898
Ours
0.937
0.913
0.939
0.913
0.900
0.842
0.833
0.883
Table 22: LPIPS on the VR-NeRF Dataset
Method
aappartment262 kitchen261 kitchen262 kitchen263 table61 workspace61 workspace62 workspace644
MonoGS
0.631
0.688
0.676
0.662
0.582
0.689
0.721
0.685
SEGS-SLAM
0.375
0.218
0.192
0.174
0.203
SRPO-GS
0.321
0.302
0.269
0.597
0.474
0.385
0.350
0.386
LongSplat
0.292
0.308
0.265
0.319
0.353
0.359
0.352
OnTheFly-NVS
0.302
01.777
0.31
0.311
0.322
0.385
0.307
0.261
Ours
0.201
0.224
0.185
0.198
0.256
0.286
0.314
0.274
26

---

Published as a conference paper at ICLR 2026
Table 23: PSNR on the KITTI Dataset
Method
00
02
03
05
06
07
08
10
MonoGS
16.01
15.08
16.90
15.66
16.38
11.38
13.21
11.82
SEGS SLAM
12.69
14.88
16.71
14.88
14.65
10.45
14.50
13.51
S3PO-GS
20.77
19.30
20.51
20.73
20.42
20.38
20.27
17.41
OnTheFly-NVS
13.97
17.00
17.34
18.08
17.29
18.21
16.50
14.73
LongSplat
17.84
14.62
17.97
18.08
18.68
16.14
16.58
14.93
Ours
23.76
22.53
24.54
23.80
23.59
23.92
22.86
20.38
Table 24: SSIM the KITTI Dataset
Method
00
02
03
05
06
07
08
10
MonoGS
0.568
0.476
0.487
0.491
0.560
0.439
0.480
0.420
SEGS- SLAM
0.454
0.458
0.448
0,470
0.510
0.405
0.493
0.462
S3PO-GS
0.732
0.587
0.581
0.659
0.652
0.715
0.684
0.545
OnTheFly-NVS
0.594
0.538
0.529
0.606
0.583
0.673
0.622
0.483
LongSplat
0.616
0.444
0.482
0.548
0.583
0.557
0.543
0.484
Ours
0.829
0.707
0.745
0.781
0.779
0.827
0.786
0.663
Table 25: LPIPS the KITTI Dataset
Method
00
02
03
05
06
07
08
10
MonoGS
0.687
0.761
0.735
0.753
0.720
0.826
0.830
0.820
SEGS-SLAM
0.492
0.491
0.465
0.453
0.464
0.552
0.450
0.533
S3PO-GS
0.264
0.459
0.498
0.389
0.401
0.364
0.345
0.563
OnTheHly-NVS
0.461
0.501
0.501
0.455
0.500
0.388
0.422
0.540
LongSplat
0.375
0.531
0.438
0.428
0.394
0.454
0.440
0.517
Ours
0.234
0.375
0.311
0.281
0.288
0.242
0.266
0.394
Table 26: PSNR on the ScanNet++ Dataset
Method
00777c41d4
02/25e5fee
0603163119
126d03d821
1chb105c6a
2284b15:9M
2428873all)
MonoGS
14.621
20.915
9,488
21.353
23.716
13.049
16.861
SEGS-SLAM
28.
S3PO-GS
21.316
25.158
21.019
23.758
26.614
23.446
23,554
OnThcFly-NVS
16.270
19.134
21.701
17.572
17.478
18.200
14.173
LongSplat
18.465
26.747
22.484
27.251
28.621
23.348
26.543
30.671
27.722
32.796
32.406
30.277
28.137
Method
303745abe7
41cb967018
46001/434d
4808c4x397
546292d9klb
712dc47104 104
7543973e1a
14,899
18.515
21.570
9,464
SLAM
27.169
S3PO-GS
25,840
21.074
20.288
24.263
20.612
17.843
26.367
OnTheFly-NVV
15.884
16.247
16.426
18.722
24.791
14.056
21,540
LongSplat
24.437
24.587
28.539
Ours
33,056
28.608
21.716
30.101
25.868
27.506
32.308
27

---

Published as a conference paper at ICLR 2026
Table 27: SSIM on the ScaNnet++ Dataset
Method
00777c41d4
02f25e5fee
0b03183119
126d03d821
lchb105c6a
2284br5c9d
2d2c873aa0
MonoCiS
0.559
0.784
0.485
0.796
0.833
0.622
0.641
SEGS-SLAM
0.911
S3PO-GS
0.695
0.853
0,809
0.828
0.876
0.833
0.8.0
OnThcFly-NVa
0,583
0.779
0,823
0.753
0,749
0.751
0.705
LongSplat
0.604
0.868
0.816
0.854
0.897
0.819
Ours
0.855
0.941
0.903
0.942
0.956
0.937
0.918
Method
303745abe7
41eb967018
460014334
4808c4a397
546292a9dh
712dc47108 104
7543973ela
MonoGi
0.723
0.848
0.244
0.590
SEGS-SLAM
S3PO-GS
0.878
0.788
0.861
0.873
0.688
0.784
0.884
OnThcFly-NVS
0,780
0.705
0.817
0.826
0.825
0.732
0.831
LongSplat
0,900
0.793
0.894
Ours
0.959
0.905
0.891
0.938
0.850
0.915
0,950
Table 28: LPIPS on the ScanNet++ Dataset
Method
00777c41d4
02/25e5fee
0513113119
126d03d821
1chb105c6a
2284h/5c9d
2d2e873aal)
MonoGS
0.822
0.724
0.370
SLSM
0.148
S3PO-GS
0.451
0.243
0,350
0.346
0,264
0.289
0.316
OnTheFly-NVe
0.524
0.352
0,310
0.404
0.409
0.386
0.499
LongSplat
0.436
0.179
0.307
0.242
0,179
0.245
Ours
0.209
0.122
0.196
0.143
0.123
0.134
0.179
Method
303745abe7
41eb967018
46001/434d
4808c4a397
546292d9Wb
712dc47104
7543973ela
MonoGS
0.707
0.619
0.485
0.408
0.734
0.707
0.441
-LAS
0.216
S3PO-GS
0.259
0.454
0.419
0.300
0.520
0.449
0.303
OnTh
0.382
0.434
0.362
0,331
0.256
0.450
0.305
LongSplat
0.265
0.286
0,200
Ours
0.115
0.165
0.279
0.156
0.205
0.176
0.140
Table 29: Ablations for Localization and Reconstruction
Method
0077 02f2 Ob03 126d
lebb 2284
2d2e
3037 41eb
4600
4808
5462
712d
7543
Avg.
w/ 3
0.305 0.192 0.576 0.310 0.153 0.320 0.582 0.582
w/ loop (vggt)
0.010 0.011 0.014 1.176 0.014 0.022 0.010 0.007 0.030
0.075 0.022
0.032
0.013
0.011 0.096
w/o loop
0.019 0.017 0.015 0.016 0.011 0.012 0.010 0.290 0.032 0.052 0.066 0.230 0.013 0.011 0.057
Track w/ MF&KF
0.011 0.027 0.012 1.050 0.010 0.008 0.014 0.005 0.022 0.076 0.040 0.024
0.013 0.010 0.094
Ours full model
0.009 0.011 0.015 0.019 0.015 0.012 0.010 0.005 0.017 0.060 0.021 0.030 0.014 0.011 0.018
Table 30: Tracking Results on the ScanNet++ Dataset
Method
0077 02f2 0b03 126d lebb 2284
2d2e
3037 4leb 4600 4808 5462
712d 7543
OnTheFly
0.804 0.157
0.114 0.227
0.227 0.913 1.438 1.186
1.605
3,932
0.486
0.018
0.458
0.907
LongSplat
1.307 0.041
0.378
0.550
0.023 0.713
1.943
0.408
0.058
MonoGS
1.364 0.626
0.294 0.939
2.019
0,922
1.760
3.540
0.432
0.853 0.642
S3PO-GS
0.650 0.016 0.093
0.418 0.135 0.441
0.355 0.858
1.674
1.318
0.614
0.909
1.092
0.270
SEGS-SLAM
0.005
0.485
MASt3R-SLAM
0.121 0.017 0.059
0.122
0.021 0.027 0.835 0.176 0.020
2.379 0.065
0.042
0.021 0.025
loop with vegt
0.010 0.011 0.014
1.176 0.014 0.022 0.010 0.007
0.030
0.075 0.022
0.032
0.013 0.011
Ours
0.009 0.011 0.015
0.019
0.015
0.012
0.010
0.005
0.017
0.060
0.021
0.030
0.014 0.011
28

---

Published as a conference paper at ICLR 2026
Table 31: Tracking Results the TUM Dataset
Method
fl 360
desk
desk2
floor plant room fl rpy fl teddy fl xyz f2 xyz f3 office
OnTheFly
0.187
0.874
4.136
0.278
0.073
1.489
LongSplat
0.133
0.712
0.103
MonoGS
0.160
0.034
0.596
0.531
0.077
0.649
0.032
0.512
0.017
0.047
0.033
S3PO-GS
0.093
0.041
0.155
0.228
0.040
0.552
0.053
0.051
0.010
0.016
0.045
SEGS-SLAM
0.154
0.016
0.013
0.293
0.006
0.026
MASt3R-SLAN
0.049
0.016
0.024
0.025
0.020
0.061
0.027
0.041
0.009
0.005
0.031
loop with vggt
0.040
0.016
0.026
0.026
0.017
0.056
0.023
0.047
0.007
0.005
0.024
Ours
0.040
0.016
0.025
0.025
0.016
0.060
0.022
0.045
0.007
0.005
0.019
Table 32: Tracking Results on the KITTI Dataset
Method
00
02
03
05
06
07
08
10
Avg.
OnThcFly
18.297
28.230
14.356
5.027
9.608
1.550
9.836
9.502
12.051
LongSplat
3.047
10.796
9.344
9,482
6.864
2.866
4.480
6.576
6.682
MonoGS
11.868
11.817
11.195
4.623
7.638
4.128
5.864
5.109
7.780
S3PO-GS
1.196
2.961
5.522
1.459
0.721
1.009
2.655
1.927
2.181
SEGS-SLAM
0.561
0.897
0.189
0.967
4.122
0.851
0.477
0.353
1.052
MASt3R-SLAN
1.756
0.397
0.761
2.279
1,361
loop with vggt
1.304
2.691
0.442
1.103
0.958
1.257
1.160
1.893
1.351
Ours
1.304
2.691
0.442
1.103
0.958
1.321
1.167
1.893
1.360
Table 33: Tracking Results on the Waymo Dataset
Method
13476
100613
106762
132384
152706
153495
158686
163453
405841
Avg.
OnThellly
1.687
1.051
2.022
14.395
1.193
2,110
2.206
1.872
1.523
3.118
LongSplat
7.024
10.242
7.506
2.616
4.900
2.625
3,921
3.939
2.729
4.956
MonoGS
3,262
6.706
15.638
11.093
8.286
0.585
6.881
10.669
3.218
7,370
S3PO-GS
1.165
2.471
0.214
1.551
1.144
1.972
0.963
1,085
0.560
1.236
SEGS-SLAM
0.860
0.696
2.755
1.867
MAStR--SLAM
2.958
1,569
1.098
2.334
0.873
loop with vegt
1,230
0.315
2.762
1.753
0.460
1.071
0.816
1.213
Ours
1.229
0.315
2.762
L.753
0.931
1.578
0.460
1.071
0.816
1.213
Table 34: Tracking Results on the TUM Dataset Compared with Non 3DGS SLAM Systems
Method
360
desk
dcsk2
floor
plant
room
rpy
teddy
xyz
Avg.
ORB-SLAM3
0.017
0.210
0.034
0.009
DPV-SLAM+
0.132
0.018
0.029
0.050
0.022
0.096
0.032
0.098
0.010
0.054
DROID SLAM
0.111
0.018
0.042
0.021
0.016
0.049
0.026
0.048
0.012
0.038
Go-SLAM
0.089
0.016
0.028
0.025
0.026
0.052
0.019
0.048
0.010
0.035
MAS13R-SLAM
0.049
0.016
0.024
0.025
0.020
0.061
0.027
0.041
0.009
0.030
Ours
0.040
0.016
0.025
0.025
0.016
0.060
0.022
0.045
0.007
0.028
29

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Published as a conference paper at ICLR 2026
SEGS-SLAM
OnTheFly-NWW
I SpiaSpiat
Ours
GI
SBPO-GS
Qurr
ST
Figure 5: More Qualitative Reconstruction Results.
02125e5fee
S183106970
MonoGS
OnT
S3PO-GS
JEGS-SLAM
MAS:3R-SLAM
Ours
Figure 6: Qualitative Comparison of Trajectories across Different Methods on the ScanNet++ Dataset.
30

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Published as a conference paper at ICLR 2026
KITT-0X
10TT-07
MonoGS
OnTheRyy-NS
S3PO-GS
SEGS-SIAM
MASI3R-SLAM
Ours
Figure 7: Qualitative Comparison of Trajectories across Different Methods on the KITTI Dataset.
PrS/PPentt
Fra/Ofiice
MonaGS
S3PO-GS
SEGS -LAM-
MASI3R-SLAN SLAM
Ours
Figure 8: Qualitative Comparison of Trajectories across Different Methods on the TUM Dataset.
31

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Published as a conference paper at ICLR 2026
Interval Probability Distribution
Deta Detribunion
0.40
Kernel Density Estimate Distribution
0.35
95% Conficence Interval
Probabiity Density
0.25
0.20
0.05
10
Distribution
Value
Figure 9: Visualization of the distribution of the dmaa attribute on ScanNet++ Yeshwanth et al. 2023 datasets.
Recobition)
antillis
Resohut vel
Ours
wio antislios
GT
Figure 10: Qualitative comparison of full- relluuotottion and low-resolution (1/8 of full-resolution) on multi-resolution Mip-NeRF360 Barron al. (2022) datasets.
32

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Published as a conference paper at ICLR 2026
Ours w/o LoD
Ours
GT
Figure 11: Qualitative Comparison of the ablation study on level-of detail on ScanNet++ Yeshwanth
al. (2023) datasets.
33


Reviews:
[{"review_id": "nxUkBHjyTl", "reviewer": "Reviewer_Xx2g", "summary": "ARTDECO provide a framework for real-time 3D reconstruction from monocular image sequences. ARTDECO aims to bridge the gap between high-fidelity per-scene optimization and feed-forward foundation models. ARTDECO achieves this by combining the efficiency of feed-forward inference with the accuracy and consistency of SLAM-based optimization, making it suitable for AR/VR, robotics, and digital twin applications.", "questions": "Question:\n1. How does ARTDECO compare with simpler baselines, such as $\\pi^{3}$ combined with bundle adjustment?\n2. In line 313, should the formula $\\alpha = (d_r - d_{max})/ (d_{max})$ given that it occurs in the rendering process?\n3. Can you provide more details on the ablation studies for mapper frames, level of detail, and structural Gaussians regarding the baseline and variant designs used?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Strength:\n1. The paper is well-written and easy to follow.\n2. ARTDECO achieve good result on the serveral benchmarks against recent baselines, providing a solid solution for 3D reconstruction", "weaknesses": "Major Weakness:\n1. The overall pipeline involves multiple stages and components, making it appear rather complex.\n\nMinor Weakness:\n1. In line 176, “front-end” should be changed to “frontend” for consistency with the rest of the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Z6fAKbpRm8", "reviewer": "Reviewer_SdRB", "summary": "I am not an expert in 3d vision and reconstruction, so the following comments and questions could be based on potentially inaccurate understandings or biased perspectives. From my understanding, this paper presents a system for efficient and high-fidelity 3D reconstruction performed \"on-the-fly\" from monocular image sequences. The method utilizes a \\textit{structured scene representation} to achieve both speed and quality, demonstrating competitive performance against state-of-the-art SLAM and reconstruction approaches like DROID-SLAM and Go-SLAM regarding metrics like ATE and memory consumption. The goal is to deliver interactive, robust 3D reconstruction in real-time scenarios.", "questions": "Could the authors elaborate on the specific architecture of the \"structured scene representation\" and quantify how its design choices (e.g., sparsity, resolution, memory layout) explicitly contribute to the claimed efficiency gain compared to unstructured or purely implicit representations?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Achieves a desirable balance between reconstruction fidelity (low ATE/RTE, good PSNR) and computational efficiency (low time and memory footprint), crucial for real-time mobile applications. \n\n2. The system's design is robust for on-the-fly operation and across diverse scene types (indoor and outdoor). The results show strong quantitative improvements or parity with several established recent baselines.", "weaknesses": "1. The abstract and snippets are light on the explicit technical details of the \"structured scene representation.\" Without knowing the specific structure (e.g., hash grid, implicit surface, etc.), it is difficult to judge the approach's novelty and engineering complexity. \n\n2. The comparison in the snippet only covers a limited subset of recent SLAM/Reconstruction methods; a more comprehensive benchmark, including performance on texture-less regions or under extreme motion, would be beneficial.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "O82uJq1Gzl", "reviewer": "Reviewer_aQRV", "summary": "The paper proposes ARTDECO, a unified online reconstruction system that integrates the feed-forward 3D model with a real-time pose estimation and loop-closure pipeline. It converts multi-view features into a hierarchical level-of-details (LoD) Gaussian scene representation and continuously refines it using all frames. This integration strikes a balance between real-time performance and reconstruction fidelity, delivering globally consistent 3D scenes with quality comparable to offline optimization.", "questions": "Please refer to the questions outlined in the weakness section.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper is well structured and easy to follow. The appendix provides more detailed experimental results and implementation details.\n\n- The work demonstrates a coherent integration of pretrained feed-forward 3D models, pose estimation, and Gaussian-based mapping into a unified online system, validated on both indoor and outdoor benchmarks.\n\n- The hierarchical Gaussian representation and LoD-aware rendering effectively balance reconstruction fidelity and computational efficiency, achieving practical real-time performance.", "weaknesses": "-The engineering efforts of the proposed system is appreciated; however, the system primarily integrates existing components/concepts from previous studies, i.e., composing feed-forward 3D foundation models (MASt3R-SLAM), Gauss–Newton BA, and hierarchical Gaussian splatting, into a single pipeline. The following points offer specific comments.\n\n- The proposed front-end tracking system is built upon MASt3R-SLAM.  It switches to a pinhole camera model + reprojection residual that is a standard bundle-adjustment design in traditional SLAM systems. This is less general than MASt3R-SLAM’s generic central camera + ray-angle residual (which is explicitly designed to be robust to depth noise and varying intrinsics). The paper does not provide analysis of failure modes under non-central cameras, zoom changes, or intrinsic effect (e.g., rolling-shutter effects), nor evidence that the chosen residual yields measurable advantages over ray-angle residuals.\n\n- LoD introduced without proper reference and sounds incremental w.r.t. previous structured/multi-level representations.\nMultiple previous works have introduced hierarchical/structured scene representations and explicit LoD mechanisms (e.g., Scaffold-GS, Hierarchical 3D Gaussians, Octree-GS, Mip-Splatting, FLoD). However, at the first occurrence of “LoD” (at Line 73), there is no clear citation for this concept in this literature. E.g., what is the difference(s) of the proposed LoD with the same concept introduced in FLoD/Octree-GS? The proposed method mainly implements a 2D patch-budget–driven coarse-to-fine scheduling (e.g., per-level pixel coverage, fixed per-level scale growth like $1.4^{2l}$, and a distance-based gating with blending). While these choices are practical, they are engineering heuristics rather than a principled spatial hierarchy. Compared to methods that anchor hierarchy in 3D space/geometry (anchors/octrees/mip anti-aliasing), the proposed 2D scheduling is arguably less principled for large-scale geometry control and view-consistent refinement/anti-aliasing. The paper does not provide a sensitivity analysis (e.g., to the 1.4 scale law, pixel-budget per level, or $d_{max}$) or an ablation against established 3D-anchored LoD schemes.\n\n- The proposed Gaussian initialization and densification seems like reusing established solutions. The method relies on residual-driven insertion to densify Gaussians, and a decoder to map multi-scale features to structured Gaussians (including scale/rotation regularization). These mirror previous practices such as base-scale setting and anisotropic covariance regularization (e.g., Meuleman et al; Wu et al, as cited).\n\n- Loop closure via a foundation 3D model (Pi3) is not conceptually new and appears to be a dominant factor. Using a foundation model for loop detection is not new (e.g., CLIP-based loop closure has been shown for monocular Gaussian SLAM, Tian et al). Here, Pi3 is simply plugged in as the retriever. The ablation indicates that replacing Pi3 with VGGT degrades accuracy, implying the BA stack is heavily dependent on the chosen foundation model rather than on the SLAM backend system itself. Moreover, although the paper mentions memory concerns of baselines (at Line 70), it does not report the compute/memory cost of employing Pi3 (or alternative foundation models) in the loop-closure module.\n\n- Please check the references; there are multiple duplicates, e.g., Longsplat: Robust unposed 3d gaussian splatting for casual long videos, On-thefly reconstruction for large-scale novel view synthesis from unposed images\n\nReferences:\n\nAndreas Meuleman, Ishaan Shah, Alexandre Lanvin, Bernhard Kerbl, and George Drettakis. Onthe-fly Reconstruction for Large-Scale Novel View Synthesis from Unposed Images. ACM Transactions on Graphics, 44(4), 2025b.\n\nSongyin Wu, Zhaoyang Lv, Yufeng Zhu, Duncan Frost, Zhengqin Li, Ling-Qi Yan, Carl Ren,\nRichard Newcombe, and Zhao Dong. Monocular online reconstruction with enhanced detail\npreservation\n\nLan, Tian, Qinwei Lin, and Haoqian Wang. \"Monocular gaussian slam with language extended loop closure.\" arXiv preprint arXiv:2405.13748 (2024).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rr17F7Dklp", "reviewer": "Reviewer_Wwfj", "summary": "The paper introduces a unified framework for on-the-fly, high-fidelity 3D reconstruction from monocular image sequences. It addresses the longstanding tradeoff between efficiency, accuracy, and robustness in real-time 3D scene capture by combining the strengths of feed-forward 3D foundation models and SLAM-style geometric optimization, using 3D Gaussian Splatting (3DGS) as the scene representation.", "questions": "See the weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is clearly written and well-structured. The problem statement (monocular on-the-fly reconstruction) and core tradeoff (efficiency vs. accuracy vs. robustness) are crisply articulated in the introduction. Figures 2 and 3 provide intuitive visual summaries of the pipeline and LoD mechanism, respectively.", "weaknesses": "1. The paper describes three modules (frontend, backend, mapping) but lacks clarity on how tightly coupled they are and whether components can be independently replaced or ablated. For example: \n  - The frontend uses MASt3R for pose estimation, but the backend uses π3 for loop closure. Why not use $\\pi_3$ in both? The ablation (Table 3) shows $\\pi_3$ underperforms in pose estimation, but the paper doesn’t explain why this is the case (e.g., is it due to training data, architecture, or metric scale ambiguity?).\n  - The mapping module relies on pointmaps from the backend, yet it’s unclear how errors in pose or loop closure propagate into Gaussian initialization.\n\n2. The paper compares against LongSplat and OnTheFly-NVS, but omits very recent pose-free methods like AnySplat or No Pose, No Problem in key metrics. More critically, the evaluation protocol (every 8th frame held out) is applied retroactively to baselines, but it’s unclear if those methods were designed for such a protocol. For example: \n - Feed-forward methods often assume all input frames are available at once; streaming evaluation may disadvantage them unfairly.\n - Conversely, SLAM methods like MASt3R-SLAM are inherently streaming—so the comparison may favor ARTDECO by design.\n\n3. The core claim is that ARTDECO “combines the efficiency of feed-forward models with the reliability of SLAM.” However, the paper does not quantify how much of the performance gain comes from: \n - The foundation models (MASt3R/$\\pi_3$),\n - The LoD Gaussian representation, \n - The SLAM-style optimization (BA, loop closure).\n\nThe ablation in Table 3 is helpful but incomplete. For instance: \n - There is no ablation removing both loop closure and mapper frames.\n - The “w/o level-of-detail” ablation shows a PSNR drop (~1 dB), but it’s unclear whether this is due to rendering artifacts, geometry errors, or memory inefficiency.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)