STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer
Authors: Yushi LAN
Yihang Luo
Fangzhou Hong
Shangchen Zhou
Honghua Chen
Zhaoyang Lyu
Bo Dai
Shuai Yang
Chen Change Loy
Xingang Pan
Venue: ICLR 2026 Poster (2026)
Keywords: Monocular and Video 3D reconstruction
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Feedforward 4D reconstruction from causal videos.

Abstract: We present STream3R, a novel approach to 3D reconstruction that reformulates pointmap prediction as a decoder-only Transformer problem. Existing state-of-the-art methods for multi-view reconstruction either depend on expensive global optimization or rely on simplistic memory mechanisms that scale poorly with sequence length. In contrast, STream3R introduces a streaming framework that processes image sequences efficiently using causal attention, inspired by advances in modern language modeling. By learning geometric priors from large-scale 3D datasets, STream3R generalizes well to diverse and challenging scenarios, including dynamic scenes where traditional methods often fail. Extensive experiments show that our method consistently outperforms prior work across both static and dynamic scene benchmarks. Moreover, STream3R is inherently compatible with LLM-style training infrastructure, enabling efficient large-scale pretraining and fine-tuning for various downstream 3D tasks. Our results underscore the potential of causal Transformer models for online 3D perception, paving the way for real-time 3D understanding in streaming environments.

Published as a conference paper at ICLR 2026
STREAM3R: SCALABLE SEQUENTIAL 3D RECON- STRUCTION WITH CAUSAL TRANSFORMER
Yushi Lan' Yihang Luo'r, Fangzhou Hong', SShannchen Zhou',
Honghua Chen', Zhaoyang Lyu", Shuai Yang', Bo Dai Chen Change Loy", Xingang Pan'
LS-Lab, Nanyang Technological University, Singapore
Shanghai Artificial Intelligence Laboratory WICT, Peking University "The University of Hong Kong
an. gi sithub.... wttppojjctssss////oooooooo strmm3r
Cache Features of View 1
Cache Features of View 1,2
Cache Features of View 1,
Figure 1: STREAM3R. Given stream of input images, method estimates dense 3D geometry for each incoming frame using causal Transformer. Features from previously observed frames are cached context for future inference. The demo video from Genie (Ball al. 2025)
ABSTRACT
We present STREAM3R, a novel approach to 3D reconstruction that reformulates pointmap prediction as a decoder-only Transformer problem. Existing state-of- the-art methods for multi-view reconstruction either depend on expensive global optimization or rely on simplistic memory mechanisms, both of which scale poorly with sequence length. In contrast, STREAM3R introduces a streaming framework that efficiently processes image sequences using causal attention, in- spired by advances in modern language modeling. By learning geometric priors from large-scale 3D datasets, STREAM3R generalizes well to diverse and chal- lenging scenarios, including dynamic scenes where traditional methods often fail. Extensive experiments show that our method consistently outperforms prior work across both static and dynamic scene benchmarks. Moreover, STREAM3R is inherently compatible with LL-Mstyle training infrastructure, enabling efficient large-scale pretraining and fine-tuning for various downstream 3D tasks. Our re- sults highlight the potential of causal Transformer models for online 3D percep- tion, paving the way for real-time 3D understanding in streaming environments.
INTRODUCTION
Reconstructing detailed 3D geometry from images is the crux in computer vision (Schonberger & Frahm 2016 Schönberger et al. 2016)) Chen et al. 202I) and serves as the prerequisite for series of downstream applications, such autonomous driving (Geiger al. 2013), virtual reality (Zheng

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et al. 2023: Lan al. 2024 robotics (Irshad al., 2024). and more. While traditional visual- geometry methods like SfM (Schonberger & Frahm 2016) and Multi-view Stereo Yao et al. 2018
2019) tackle this problem by solving series of sub- sub-problems through handcrafted designs, recent trend led by DUSt3R (Wang et al. 2024d) has demonstrated a promising new way of directly re- gressing point clouds using powerful transformers. This paradigm, along with its follow-up works including MASt3R (Leroy et al. 2024), Fast3R (Yang et al.) 2025), ). and VGG-T (Wang et al. 2 enables the reconstruction of 3D geometry from number of input images ranging from a single image to hundreds offering more unified solution to 3D reconstruction.
While these works focus on processing a fixed set of images, real-works applications often require continuously processing streaming visual input and updating the reconstruction on-the-fly (Davi- son et al. 2007), such as when an autonomous agent explores a new environment, or when pro- cessing a long video sequence. Handling streaming input poses significant new challenges. For example, naively running Fast3R or VGG-T every time new image arrives would incur signifi- cant redundant computation, as they have to reconstruct from scratch without inheriting previous results. These methods also struggle with long videos due to the expensive full-attention operation. Spann3R (Wang & Agapito 2024) extends DUSt3 with memory design (Cheng & Schwing 2022) to support incremental reconstruction, but it still suffers from significant accumulated drift and fails over dynamic scenes. The most relevant concurrent work is CUT3R (Wang al.) 2025b), which proposes a RNN paradigm (Zaremba et al. 2015) to handle unstructured or streaming inputs. However, the RNN-based design does not scale well with modern network architectures (Dao 2024)
and struggles with long-range dependency due to its limited memory size.
In light of the streaming nature of this task, in this work, we are interested in investigating the use of transformer with uni-directional causal attention to achieve online, incremental 3D reconstruc- tion. In an LLM-style transformer with causal attention, the prediction at each step reuses previous computations through KVCache, which has been proven successful in many language and audio tasks (Touvron et al., 2023, Copet et al., 2023). We observe that this property is also highly desir- able for addressing online 3D reconstruction from streaming data, as each step should build upon the previous reconstruction while integrating new content from the incoming frame.
Motivated by this, we propose STREAM3R, framework that performs 3D reconstruction from unstructured or streaming input images, and predicts the corresponding point maps in both world and local coordinates (Yang et al. 2025). Unlike concurrent works (Yang et al. 2025 Wang al., 2025a) that resolve this issue by replacing DUSt3R's asymmetric decoders with bi-directional at- tention blocks (Devlin al. 2019: Brooks et al.
2024). STREAMMR follows the modern decoder- only (Brown et al.] 2020) transformer design, where incoming frames are sequentially processed and registered with causal attention (Chen et al.
2025). In this way, STREAM3R is naturally com-
patible with modern Large Language Models Ms) (Touvron et al. (2023) training and inference
techniques such as window attention (Jiang et al. 2023) and KVCache (Brown et al. (020), i.e., the
tokens of processed observations will be saved as reference for registering incoming frames.
We train our method end-to-end on a large collection of 3D data, and benchmark the proposed method on a series of downstream applications. In summary, our key contributions are as follows: (1) We propose STREAM3R, decoder-only transformer framework that reformulates dense 3D re- construction sequential registration problem with causal attention, enabling scalable processing of unstructured and streaming inputs. (2) The design is naturally compatible with modern LLM- style training and inference paradigms, allowing efficient context accumulation across frames. (3) Our architecture supports both world- and local-coordinate pointmap prediction and extends seam- lessly to large-scale novel view synthesis through splatting- based rendering; trained and-to-end on diverse 3D data, STREAM3R achieves competitive or superior performance on standard benchmarks with strong generalization and fast inference.
RELATED WORK
Classic 3D Reconstruction. Early 3D reconstruction pipelines such as Conetturre-roo-Moiiiioo (SfM) (Hartley & Zisserman, 2003 Schonberger & Frahm 2016: Tang & Tan 2018) and SLAM (Davison al. [007] Mur-Artal et al. 2015; Teed & Dengi (021) estimate sparse ge- ometry and camera poses from image collections via geometric reasoning. More recent approaches
such NeRF (Mildenhall al. 2020 Zhang al. 2020 Wang et al. 2021a) and Gaussian Splat-
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ting (Kerbl et al. 2023 Huang et 2024 shift the focus to high-fidelity novel view synthesis using continuous volumetric representations. However, these methods are typically trained per-scene with no learned priors, leading slow convergence and poor generalization to sparse or occluded inputs -a limitation sometimes referred to as the tabula rasa assumption (Wang et al. 2025b). In contrast, adopt data-driven approach that learns geometric priors from large-scale 3D datasets (Ling) iet al., 2024, Reizenstein et al. 202I), enabling fast and generalizable reconstruction from unstruc- tured or streaming inputs.
Learning 3D Priors from Data. Recent works leverage large-scale data to learn priors for depth es- timation Yang al. 2024b; Ke et al., [024] Hu al. (025), ) pose+depth estimation (Li al. 2024] Wang et al. 2024b), and bundle adjustment (Wang et al. (024a)). While these methods improve gen-
eralization, most focus on monocular depth or view setups, limiting their ability to reconstruct full geometry in the absence of known intrinsies (Yin et al., 2023). VGGSfM (Wang et al. 2024a) introduces differentiable bundle adjustment by integrating neural feature matching with classic op- timization, but remains iterative and computationally heavy, impeding scalability. In the multi-view stereo domain, approaches such as MVSNeRF (Chen et al. 202T: 2024) and MVSNet (Yao et al.) 2018) integrate neural networks into the MVS pipeline but typically require known camera poses and still heavily rely on hand-crafted components to effectively incorporate 3D geometry.
canntmap-based Representations. Pointmap-basee representations (Wang et al. 2024d: Leroy 2024 Charatan et al., 2024] eu al. 2024 Szymanowicz et al.) 2023 Zhang et al.) 2024a b
et al.
Fang et al. 2026) have recently emerged
unifying format for dense 3D geometry prediction,
aligning well with the output structure of neural networks. Compared to voxels (Sitzmann et al.
2019). meshes (Gkioxari al. 2019), or implicit fields (Park et al., 2019 Mildenhall et al. 2020), pointmaps enable feedforward inference and real-time rendering, and can directly support applica- tions such as rasterization- based rendering (Kerbl et al. 2023). SLAM (Murai et al. 2024 Liu al. 2024), and few-shot synthesis (Ye et al., 2025). DUSI3R (Wang et al. (022dd) and follow-ups like MASt3R (Leroy et al. 2024) recast stereo 3D reconstruction as dense pointmap regression, jointly estimating depth, pose, and intrinsics from image pairs. However, their pairwise design fundamen- tally limits scalability - requiring quadratic fusion operations and complex global alignment pro- cedures when handling multi-view scenarios. Our approach maintains the advantages of pointmap representations while overcoming these scalability limitations.
4D Reconstruction from Monocular Videos.
Reconstructing dense geometry of dynamic scenes from monocular video is significant but challenging for conventional methods. Recent methods (Lei) al. 2024 Chu et al., 22024] Li et al. 2024| Kopf et al., 2021) leverage depth priors resolve this
challenge. Specifically, Robust-CVD (Kop' et al. 2021 and MegaSAM (Li et al. 2024) require
time-consuming per-video optimization. MonST3R 3R (Zhang et al.) 2024a) builds DUS13R to output pointmaps for dynamic scenes by fine-tuning DUSI3R on the dynamic datasets. However, it still requires a sliding-window based per-video global alignment as post-processing. In contrast, our method enables feedforward 4D reconstruction directly from monocular videos, supporting online prediction without costly per-video optimization or post- post-processing alignment.
Reconstruction Methods from Streaming Inputs. Streaming approaches offer a more scal- able alternative solution for the 3D reconstruction problem, represented by the monocular SLAM pipelines (Davison et al., 2007, Liu et al. |024] /Zhu et al. 2024). Inspired by the existing learning- based online 3D reconstruction methods (Chyy et | 2016 Yu et al. 2021 Wang et al. 2021c), re- cently Spann3R (Wang& & Agapito, 2024) introduces memory-baset extension to DUS(3R, while Fast3R (Yang et al.J 2025) and VGG-T (Wang et al.] 2025a) replace asymmetric decoders with Transformer-basee attention stacks directly enable multi-view fusion. Despite these advances, these approaches still predominantly rely on global full-attention mechanisms, limiting their real- time scalability with increasing sequence length. CUT3R (Wang et al.) (2025b) adopts an RNN-style architecture to process unstructured inputs incrementally. but suffers from limited memory capacity and poor compatibility with modern hardware acceleration techniques (Dao 2024). Our method fundamentally re- re-conceptualizes pointmap prediction as decoder-only Transformer task, enabling efficient causal inference through techniques like KVCache and windowed attention (Jiagg et al.)
2023 Brown et al., 2020). This architectural design allows to scale effectively to long sequences while maintaining full compatibility with modern LLM- training infrastructure and optimiza- tion techniques, overcoming the limitations of previous approaches.
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Two Views
Art
Munsory Cac
Jesage
Soca
VIT Encoder
Imsge
Mirearr- Ei-
Imagen
Self Auaa
Cansod Amn
Lood
Streaming Iuput Images
Reconstructions
Figure 2: Method overview. Built on a causal transformer, STREAM3R processes streaming images sequentially for 3D reconstruction. Each imput image is first tokenized using shared ViT encoder, and the resulting tokens are passed to our causal decoder. Each decoder layer begins with frame-wise self-attention. For subsequent views, the model applies causal attention to the memory tokens cached from previous observations. The outputs include point maps and confidence maps in both world and camera coordinate systems, long the camera pose as shown on the right. Note that we visualize the point cloud of the Headhocal with its depth map.
PRELIMINARIES: DUST3R
We reformulate DUS13R (Wang et al. 2024d to process streaming images. In DUS(3R, each incoming image I, is patchified into K tokens weight-sharing ViT encoder Dosovitskiy et al. 2021). The model operates on two views at time { (1,,)), producing token sets Fi and F2. Two symmetric decoder branches then perform cross- aross-attentive reasoning through B transformer blocks, defined recursively as G; Blockkr ,,, for = 1,..., B, with initialization G. = F. Finally, each branch predicts a pointmap and confidence map (X,,, C. Head, G. DUSt3R inherently limited to two-view inputs and relies on expensive global alignment procedure to incorporate ad- ditional views.
Encoder(I.) with F, E RKxC' using
4 METHOD
We introduce STREAM3R, transformer that ingests uncalibrated streaming images as inputs and yields series of 3D attributes as output. The input can be either unstructured image collections or video. Unlike existing approaches (Wang et al. 2025a Yang et al.) (025) that address this issue by adopting costly bi-directional attention over the entire input sequence or using fixed-size memory buffers (Wang & Agapito 2024 Wang et al. 2025b), STREAM3R instead caches features from the past frames as context and processes incoming frames sequentially using causal attention over the accumulated observations. This design not only enables faster training and quicker convergence but also aligns with the architectural principles modern LLMs, allowing us to leverage the advances of that domain. We first introduce the problem formulation in Sec. 4.1 the architecture in Sec. 4.2 and the training objectives Sec. 4.3 and the implementation details in Sec. [ An overview of the proposed method is shown in Fig. 2 Also note that STREAM3R shares the same architecture design with DUSt3R, and please refer to the appendix for the preliminaries.
4.1 PROBLEM DEFINITION AND NOTATION
STREAM3R a regression model that sequentially takes a stream N RGB images (I) where each image I H belongs to the same 3D scene. The streaming inputs successively

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transformed into set of 3D annotations corresponding each frame:
(1) Technically, STREAM3R is implemented causal transformer that maps each image I, into its corresponding pointmap of the local coordinate X xx and its pointmap in global coordinate X E R33HHxW, which is defined with respect to the camera coordinate of the first input frame I,, and its relative camera pose P. E R including both intrinsics and extrinsics. We devise later how these 3D attributes are predicted.
fo((T) = (Xt X P:).
4.2 CAUSAL TRANSFORMER FOR 3D REGRESSION
Causal Attention for Long-context 3D Reasoning, As mentioned in Sec. given the streaming inputs, for each current image. I, our method first tokenizes it into the features F. EEncderr [ The main difference lies in the decoder side: rather than performing bi bi-directional attention over the whole sequence (Yang et al.) 2025) or interacting with learnable state as in Wang al. (2025b), we draw inspiration from the I.MMs ('ouurro et al. 2023 Brown et al. 2020) DeepSeek-AT et al. 2024) and perform causal attention efficiently with previous observations. Specifically, after performing frame-wit wise self- aff-aattetionn in each decoder block, the current feature G will cross-attend to the features of previously observed frames corresponding to the same layer:
(2) This interaction ensures efficient information transfer to handle long-context dependencies. Note that this operation easy to implement and well optimized with KV cache during inference for efficient computation (Brown et al. 2020 Touvron et al. 2023).
G DecoderBlock' (Gi-1, G @ G ..... G
Simplified Decoder Design. To achieve this, several network architecture modifications are re- quired. In DUSt3R, the decoder follows a symmetric design, i.e., two separate decoders Decoder,, Decoder2 are employed handle two input views. To extend to an arbitrary number of inputs, we remove the symmetric design and only retain single decoder Decoder to process all the input frames. Specifically, each block in the decoder contains SelfAttn block for frame-wise attention, and GrossAttn block for causally attending to the features of all previous observations. Note that we process the first two frames following the convention of DUSt3R due to the lack of historical context. All incoming frames afterwards follow the causal operation in Eq. (2). Note that to indicate the canonical world space, we add a learnable register token [reg] to the tokens Fi Fi freg], in element wise manner, shown in Fig. In this way, the model learns to output the global points without introducing N separate decoders. Unlike Yang et al. (2025). we did not impose positional embedding for other frames for simplicity.
the first frame
Prediction Heads. After the decoding operation, the 3D attributes corresponding to each frame can be predicted accordingly. Following existing works (Wang al.) 2025b a), predict two sets of point maps !ocal X with their corresponding confidence maps Clical Egloobal Specifically, the local point map X local is defined in the coordinate frame the viewing camera, and the global point map slobal is in the coordinate frame of the first image I1. We use two DPT (Ranftl et al. 2021 heads for point map prediction:
X Ójpcal Head GB).
global Ealobal Head G
G
(4)
P. = pose Go
G()),
(5)
where this redundant prediction has been demonstrated to simplify training (Jiang al. 2025) and facilitates training on 3D datasets with partial annotations
(Liu et al. 2022 Yu et al. 2023)
4.3 TRAINING OBJECTIVE
STREAM3R trained using a generalized form of the pointmap loss introduced in DUS(3R Given a sequence of N randomly sampled images, sourced either from a video or an image collection, we
train the model to produce pointmap predictions denoted by X' = { Lggoohal ). where p
X local Te and Pglobal { { N 1==1- The corresponding confidence scores are denoted as Ć.
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Input Tmages
Fast3R
Ours
Figure 3: Qualitative results on in-the-wild images. We compare our method, STREAM3R". with MonST3R, Fast3R, and CUT3R, and demonstrate that it achieves superior visual quality.
Following Wang et al. (2025a). we apply a confidence-awarc regression loss to the pointmaps: Lconf = (*.,)))(&.C)) (2- 12 - = - a log where and are scale normalization factors for & and for scale-invariant supervision (Wang al. 2024c). We also set $ metric-scale datasets in MASI3R (Leroy et al.) 2024) to enable metric-scale pointmaps pre- diction. For the camera prediction loss, parameterize pose P. as quaternion qi, translation A, and focal f,, and minimize the L. norm between the prediction and ground truth: Siue
for
Di=1 (l q: 4:122 +
N
ft f:(l))
EXPERIMENTS
Datasets. We train our method on large and diverse collection 3D datasets, c.g.,
Co3Dv2 Reizznsteei al. 2021). ScanNet++ (Yeshwanth et al. (023). ScanNet (Dai et al.
2017 HyperSim (Roberts et al. 2021). Dynamic Replica (Karaev et al. 2023), DL3DV (Ling
al. 2024). BlendedMVS (ao al. 2022)), Aria Synthetic Environments (Pan et al. 2023 Tar-
Aan Air (Wagg et al. 2020), MapFree (Arolld et al., (202)), MegaDepth (Li & Snavely 2018 and
ARKitScenes (Baruch et al. 2022). Please check the appendix for the full dataset details.
Implementation Details. We provide two versions of STREAM3R, where STREAM3R in- spired and fine-tuned from DUS(3R (Wang
2024d) pre-trained weights, and STREAM3R is initialized from the flagship VGG-T (Wang et al. 2025a) model. For STREAM3R we inherit al. (222)) as encoder, and retrofit its 12-layer decoder
the 24-layer CroCo ViT (Weinzaepfel network by only retaining the first decoder Decoder = Decoder The DPT-L (Ranftl et al.) 202T heads are used map the decoded tokens the local and global point maps accordingly. For STREAM3R we replace the SelfAttn layer in the Global Attention of VGG-T with CausalAtin and fine-tune all the parameters. For memory- and stable training, we inject QK-Norm DDe- lghanni et al. 2023) to each transformer layer and leverage FlashAttention (Dao 2024 for B6 mixed precision training.
Training Details. Our model is trained with the AdamW optimizer batch size of 64 with learning rate le-4 for 400K iterations. For each batch, we randomly sample 4 - 10 frames from a random training scene. The input frames cropped into diverse resolutions, ranging from 224 x 224 to 512 x 384 to improve generalization. The training runs end-to-end on 8 NVIDIA A100 GPUs over seven days. Gradient checkpointing is also adopted to optimize memory usage.

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Table 1: Single-frame depth evaluation. We report the performance on Sintel, Bonn, KITTI, and NYU-v2 (static) datasets. The best and second best results in each category are bold and underlined respectively. Our method achieves better or comparable performance against existing methods.
Sintel
Bonn
KITTI
NYU-v2
Method
Abs Rel 4 6<11.25
Rel
6c1 122
Abs
6<1.22
Abs Rel | 6<11.25
VGG-T
Wane et al.
2036
0.271
67.7
0,053
97.3
0.076
93.3
0.060
94,8
3R
Tamy
0.502
$2.8
0.192
77.3
0.129
81.2
0.099
88.9
DUSI3R
Wang el mi
20240
0.424
58.7
0.141
82.5
0.112
86.3
0.080
90.7
MAS:3R
Leroy
2024g
0.340
60.4
0.142
82.0
0.079
94.7
0.129
84.9
MonST3R
Zring 2r
2022aa
0.358
54.8
0.076
93.9
0.100
89.3
0.102
Spann3R Wang & Asapito 2024
0.470
53.9
0.118
85.9
0.128
84.6
0.122
84.9
CUT3K
[Wang et ale 20255
0.428
55.4
96.2
0.092
91.3
0.086
STREAMEK
0.350
$9.0
0.075
93.4
0.088
21.
0.091
89.9
STREAM3R
0.228
70.7
0.061
96.7
0.063
95.5
0.057
95.7
Baselines. We compare our methods against a set of baselines that are designed to take a pair of views as input: DUSt3R (Wang et al. (2024d), MASS3R (Leroy al.] (2024). and MonST3R (Zhang et al. 2024a). Besides, we include the comparison against concurrent methods Spann3R (Wang & Agapito| (024)) CUT3R (Wang et al.) 2025b), SLAM3R (Liu al., 2024), and Fast3R (Yang a 2025) that are specifically designed for handling a varying number of input images. We also include the flagship 3D geometry model VGG-T (Wang et al. 2025a) for reference. Note that Fast3R and VGG-T are bi-directional attention methods, and we group them together with methods that require global optimization (GA). We group other concurrent methods together as streaming methods that support processing sequential inputs. Note that for all methods except for VGG-T T and STREAM3R we conduct inference with the largest dimension of 512. For VGG-T based methods, we conduct inference with the largest dimension of 518 due to the requirement of DINO-V2 tokenizer (Oquab) itt al., 2023). Regarding FPS, we benchmark the inference speed on the A100 GPU with FP32. Comparisons of more concurrent methods (Zhuo et al. 2025 Yang et al. 2024b) are included in the appendix.
5.1 MONOCULAR AND VIDEO DEPTH ESTIMATION
Mono-Depth Estimation. Following previous methods Zhang al.) 2024a Wang al. 2025b)
we first evaluate monocular depth estimation on Sintel (Butler et al.] 2012). Bonn (Palazzolo et al. 2019) KITTI (Geiger et al., 2013). and NYU-v2 (Silberman et al., 2012) datasets, which cover dynamic and static, indoor and outdoor, realistic and synthetic data. These datasets are not used for training and are suitable for benchmarking the zero-shot performance across different domains. Our evaluation includes the absolute relative error (Abs Rel) and percentage of inlier points within a ff25-f of true depth 5 < 1.25, following the convention of existing methods (Hu et al.) [025]] Yang et al. 2024a). Per-frame median scaling is imposed as in DUS(3R. We include the quantitative results in Tab. I As can be seen, our method achieves state-of-the-ee compared to seraming-basse methods, and even performs best compared to VGG-T on Sintel, KITTI, and NYU-2. Also note that our method uses fewer datasets and compute resources compared to CUT3R. Specifically, CUT3R adopts curriculum training of four stages for 100 + 35 40 + 10 185 epochs, while our method is trained end-to-end for only epochs using a partial of CUT3R's 's datasets due to the computational resources constraints.
Video Depth Estimation. We also benchmark our model on the video depth task, which evalu- ates both per-frame depth quality and inter-frame depth consistency by aligning the output depth maps to the ground truth depth maps using a given per-sequence scale. Metric point map methods like MAS13R, CUT3R, and ours are also reported without alignment. The quantitative results for both methods are included in Tab. 2 Over per-sequence scale aligmment, our method surpasses optimization- baselines DUSL3R-G. (Wang et al.] (024d) and MASt3R-GA (Leroy et al.) 2024) (static-scene assumption) and even MonST3R-GA (Zhang et al. 2024a) (dynamic-scene, op- tical flow (Teed & Deng. 2020) dependent). Against the streaming state-of-the-art CUT3R, we achieve higher accuracy on all three benchmarks while running 40% faster. STREAM3R also outper- forms full-attention Fast3R (Yang et al., 2025), streaming approaches Spann3R (Wang & Agapito 2024), and the flagship model VGG-T on Sintel. Notably, STREAM3R using sliding-windie attention (Jiang et al. 2202) for constant cache, exceeds STREAM3R on Bonn and KITTI despite accessing only five past frames.
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Video depth evaluation. We evaluate scale-invariant and metric depth accuracy on the Sintel, Bonn, and KITTI datasets. Methods that require global alignment are denoted "GA"" The "Type" column indicates whether the method is Optimization-aase ("Stream"), or full-attention ("FA"). We also report inference speed in FPS on the KITTI dataset using 512x 144 resolution for all methods on an A A00 GPU, except for Spann3R, which supports x24x224 imputs. Our method achieves performance that better than CUT3R, while offering faster inference. STREAM3R CWM.MMR/--WWBB. _W[5] uses sliding window attention on STREAM3R with window size 5. Note that STREAM3R - -W[5] achieves the fastest FPS among all streaming- based methods.
Table 2:
("Optim"), streaming
Sintel
Bonn
KITTI
Aligmment
Type
Abs
<
Abs
8
Abs
8
Rel
1.25 1
Rel 4
1.25
Rel J.
1.25
VGG-T
Wang al
2
FA
0.297
68.8
0.055
97.1
0.073
96.5
7.32
Fast3R
Tang
44.9
0.193
77.5
47.23
DUSER GA
Wang a. 2124d
Optim
0.656
45.2
0.155
83.3
0.144
81.3
0.76
MAS0R
Letoy
Optim
0.252
70.1
74.5
0.31
Per sequence scale
MonST3R:CC GA
Zhang et
2024a
Optim
0.378
55.8
0,067
96.3
0.168
74.4
0.35
Spann3R Wang & Agapito 2024
Stream
0.622
42.6
0.144
81.3
0,198
73.7
13.55
CUT3K
Wang et al. 20256
Stream
0.421
47.9
0.078
93.7
0.118
88.1
16.58
S STREAMIR
Stream
0.478
51.1
0.075
94.1
0.116
89.6
23,48
STREAM3R
Stream
0.264
70.5
0,069
95.2
0.080
94.7
12.95
STREAM3R W[[]]
Stream
0.279
68.6
0.064
96.7
0.083
95.2
32,93
MAS:3- GA
1 Leroy at 2024
Optim
1.022
14.1
0,272
70.6
0.467
15.2
0.31
Metric scale
CUT3R
Wang et al 20256
Stream
1.029
23.8
0.103
88.5
0.122
85.5
16.58
S STREAME
Stream
1.041
21.0
0.084
94.4
0.234
57.6
23,48
Table 3: 3D reconstruction evaluation on 7-Scenes (Shotton al. 2013 Despite operating in the
streaming setting, our method delivers competitive performance, matching or even exceeding that of offline optimization-oon based methods that leverage global alignment.
Acc|
Comp 1
NC1
Melhod
Type
FPS
Mean
Med.
Mean
Med.
Mean
Med.
VGG-T
Wane et al
2025a
FA
0.087
0,039
0,091
0.039
0.787
0.890
12.00
Fast3R
Yang el al 2025
FA
0.164
0.108
0.163
0.080
0.686
0.775
30.92
DUSI3R GA
Wang
21244
Optim
0.146
0.077
0.181
0.067
0.736
0.839
0.68
MASOR-GA
Leroy et al. 2114
Optim
0.185
0.081
0.180
0.069
0.701
0.792
0.34
MonST3R-GA
Zianng
Optim
0.185
0.266
0.167
0.672
0.759
0.39
Spann3R Wanc Asapito
2024
Stream
0.298
0.226
0.112
0.730
12.97
SLAMEK
et al
2044
Stream
0,287
0.155
0.226
0.066
0.644
0.720
38.40
CUT3R
Wang et 20220
Stream
0.126
0.047
0.154
0.031
0.727
0.834
17.00
S STREAM3R
Stream
0,148
0.077
0.177
0.058
0.700
0.801
26.40
STREAM3R
Steeam
0.122
0.044
0.101
0,038
0.746
0.856
20.12
5.2 3D RECONSTRUCTION
We also benchmark scene-level 3D reconstruction on the 7-scenes (Shotton et al. 2013) dataset and use accuracy (Acc), completion (Comp), and normal consistency (NC) metrics, following the convention of existing methods (Wang & Agapito| 2024 Wang et al. 2025b 2024d). Following CUT3R, we assess the model's performance on image collections with minimal or no overlap by evaluating using sparsely sampled images, i.e., 3 to 5 frames per scene. The quantitative results are included in Tab. 3 Our method achieves better performance compared to optimization- methods and strong baselines including Spann3R, Fast3R, CUT3R, and SLAM3R. Compared to CUT3R, our method shows better performance with over 50% times faster during the inference. While SLAM3R achieves the fastest inference, it yields noticeably lower reconstruction accuracy than our method. This performance gap can be partially attributed to SLAM3R being trained and evaluated at lower input resolution of 224 x 224. The comparison results NRGBD (Azinović et al. 2022 benchmark are included the appendix.
5.3
ABLATION ON THE EFFECTIVENESS OF THE PROPOSED ARCHITECTURE
Here, conduct detailed ablation analysis STREAM3R demonstrate the effectiveness of its designs. Due to the extensive computational resources required to train the model, we only train the ablation models 224 x 224 resolution images. All the datasets included train the models.
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0.7
Ours CUT3R
Local Elead Leas
Ours CUT3R
0.6
WW hy
Ours CUT3R
0.6
Olobal Head
0.5
Total
0.4
0.4
W
0.2
5000
10000
15000
20000
10000
15000
20000
L00D0 15000
20000
Training Step
Training Step
Training Step
(a) Overall Training Curve
(b) Curve of Local Branch
(c) Curve of Global Branch
Figure 4: Ablation of our proposed STREAM3R. Compared to Wang et al. (2025)) our decoder- only network yields better convergence with faster training speed in the 3D point map prediction task, especially in the global branch.
For a fair comparison, we initialize all the models below using the pre-trained MASt3R (Leroy et al., 2024) checkpoints and train the models using the same typer-parameten and compute resources.
We demonstrate the effectiveness of onclder-only transformer against RNN design in the sequential 3D pointmap prediction. The main baseline is CUT3R (Wang al. 2025b), which leverages the RNN design to achieve this. For a fair comparison, we re-train CUT3R and our method using the same dataset and pre-trained model weights initialization. We include the training curve in Fig. 4a where both models are trained with the same hyperparameters and compute resources. As can be observed, STREAM3R converges faster compared CUT3R and performs 60% more training steps within the given time. This may sound counterintuitive since STREAM3R is attending to longer context against CUT3R's constant state memory. However, since CUT3R architecture requires state-update operation after each state-readout interaction, while STREAM3R directly attends to cached features of existing observations.
We also notice in Fig. 4b that the convergence of Head is similar among the two architectures, while for Headglobal our proposed architecture shows noticeably faster convergence speed, as shown in Fig. 4c This demonstrates that using single state makes the model harder to register incoming frames due to the limited memory capacity.
Quantitatively, benchmark the ablation models on both the video depth estimation and 3D construction in Tab. which evaluates the Headhocal and Headgiobal correspondingly. For a fair comparison, evaluate the checkpoints trained for the same number of iterations. As can be ob- served, our proposed architecture consistently achieves better performance on both tasks.
Table 4: Ablation video depth estimation and 3D reconstruction. Comparison between RNN- based CUT3R and our proposed architecture STREAM3R". Results show consistent improvements across both video depth estimation (Sintel, BONN, KITTI) and 3D reconstruction (7-Scenes).
Video Depth Estimation
3D) Reconstruction (7-Scenes)
Method
Sintel
BONN
KITTI
Acc|
Compl
NC1
Abs Rel
< 1.25 Ahs Rel 5 < 1.25 Abs Rel < 1.25
Menn
Med.
Mean
Med.
Mean
Med.
CUT3R
0.598
40.7
0.102
90.7
0.157
77.4
0.480
0.365
0.330
0.148
0.555
0.583
STREAM3R
0.535
47.0
0.083
94.2
0.141
81.8
0.328
0.261
0.255
0,095
0.605
0.659
5.4
MORE ANALYSIS
Memory Usage. Tab, filuustilssss the peak GPU memory usage comparison under different numbers of input frames. All measurements are conducted on a single NVIDIA A100 GPU using FlashAt- tention (Dao) 2024). with input image resolution set to 448 x 448. While naive attention imple- mentations cause quadratic memory usage with respect to sequence length, FlashAttention reduces this from quadratic to linear. Unlike bi-directional methods that process all views jointly, causal version processes streaming views sequentially, resulting in linearly increasing KV Cache memory.
Our method naturally supports sliding window attention without requiring any fine-tuning, We implement STREAM3RR - a window attention mechanism that always attends to the features of the first frame and the five most recent frames from previous observations. With this approach, the KV Cache size remains constant regardless of input sequence length. As shown in Tab, 2 using window attention achieves comparable even better performance in video depth evaluation.

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Published as a conference paper at ICLR 2026
Robustness of the Anchor View. Using the first frame the global coordinate system is stan- dard convention across DUSt3R and its uo works, including MASt3R, MonST3R, CUT3R, VGGT, and ours. As shown in the Fig. 7p, even when the first frame has very little over- lap, our model still shows strong implicit relative pose-learning capability for the other views.
Quantitatively, we further follow the degradation pipeline of Real-ESRGAN (Wang al.) 2021d)) to corrupt the first frame of each sequence, and
Table 5: GPU memory usage comparison (GB).
Input Frames
100
VGG-T CUT3R
4,70
9.99
18.66
30.48
45.47
63.63
then evaluate VGGT, CUT3R, and our method on
3.34
3.71
4.11
4,48
4.86
5.25
the 7-Scenes dataset. This directly examines sce- narios where the first frame is low-quality. As shown in Tab. TO all methods experience some degradation. However, CUT3R's Accuracy error increases markedly from 0.126 to 0.335, whereas that of STREAM3R rises only from 0.122 to 0.223, indicating that our method considerably more robust under such challenging conditions. We further add visualizations for unordered image inputs and even the case with the non-overlapping anchoring view in Fig. Fig.7. Fig. demonstrates that STREAM3R also performs well on unordered inputs, beyond the streaming setting. Fig. 7 further shows that when the first frame has very little overlap, our model still yields strong implicit relative pose-learning capability for the other views.
MonST3R-GA
3,05
12.36
22.52
32.69
42.81
52.96
STREAMER
10.98
16.32
STREAMER
6.29
8.71
11.83
14.95
18.08
STREAMER" -W1S!
3.02
3.72
3.72
3.72
3.72
3.72
STREAMER W[[]]
4.70
5.18
5.18
5.18
5.18
5.18
5.5 MORE APPLICATIONS
Integration with Novel View Synthesis. We demonstrate the utility of our method for down- stream applications by integrating it with Novel View Synthesis. Specifically, we utilize the dense point maps and camera poses predicted by our model as a geometric prior to initialize 3D Gaus- sian Splatting (Kerbl al. 2023). By exporting
predictions to COLMAP-compatibe for- mat (Schonberger Frahm 2016), we enable the effective optimization 3D Gaussians on com- plex video sequences without relying external Structure-front from-Motion tools. As shown in Fig. 5 STReAM3R Riinitialized point clouds and camera poses facilitate high-quality novel view renderings.
Input Views
Reconstructed Point Cloud
Rendered Novel Views
Figure 5: Visualizations of our reconstructed point cloud and rendered novel view from 3D Gaussian Splatting Kerbl et al. 2023).
CONCLUSION
We have introduced STREAM3R, a decoder-only transformer framework for dense 3D reconstruc- tion from unstructured or streaming image inputs. By reformulating reconstruction sequential registration task with causal attention, STREAM3R overcomes the scalability bottlenecks of prior work and aligns naturally with LLM--ttll training and inference pipelines. Our design allows ef- ficient integration of geometric context across frames, supports dual-coordinate pointmap predic- tion, and generalizes novel-view synthesis over large-scale scenes without requiring global post- processing. Through extensive experiments across standard benchmarks, we show that STREAM3R achieves competitive or superior performance in the wawpeuularvvieoodeeeeee estimation and 3D re- construction tasks, with significantly improved inference efficiency. By bridging geometric learning with scalable sequence modeling, we hope this work paves the way for more general-purpose. real- time 3D understanding systems. Please refer to appendix for the limitation discussion.
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Published as a conference paper at ICLR 2026
Acknowledgement. This research is supported by the National Research Foundation, Singapore, under its NRF Fellowship Award TRF-NRFF16-2024-0 2024-0003. This research is also supported by cash and in-kind funding from NTU S-Lab and industry partner(s).
REPRODUCIBILITY STATEMENT
We exclusively use publicly available datasets for model training, with complete details provided in the paper. All code and model checkpoints are released here
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A APPENDIX
A.1 USE OF LARGE LANGUAGE MODELS
Large Language Models (LLMs) are used exclusively for minor grammar corrections and stylistic polishing of the manuscript. They are not involved in the design of the methodology, execution of experiments, analysis of results, or any other aspect of the scientific contribution.
A.2 DATASET DETAILS
We train our model on 29 datasets that contains diverse range of scene types, including static and dynamic scene and objects. Specifically, we mainly follow the data splits of CUT3R (Wang al.,
2025b), and the main 15 datasets with highest sampling ratio are: Co3Dv2 (Reizenstein et al. 2021
ScanNet++ (Yeshwanth et al.) (023). ScanNet (Dai et al., 2017). HyperSim (Roberts et al.
2021
Dynamic Replica (Karaev et al. 2003), DL3DV (Ling et al. 2024), BlendedM VS Yao et al.
2020
Aria Synthetic Environments (Pan et al. 2023). TartanAir (Wang et al., 2020 MapFire (Arnold
et al. 2022), MegaDepth (Li & Snavely 2018). WildRGBD (Xia et al. (222)) Waymo (Sun et al.
2020 Bedlam (Black et al. 2023), and ARKitScenes (Baruch et al. (022). We do not include 3D
Ken Burns (Niklaus et al. 2019), IRS (Wang al.) (2021b) and SmartPortraits (Korniooa et al. 2022) for training since these datasets are either single view or fail to download successfully. We adapt the official scripts provided by CUT3R (Wang et al. 2025b), DUSt3R (Wang al. 2024d) and Spann3R (Wang & Agapito 2024 for dataset processing. For training STREAM3R we re- move all the single-view datasets in VGG-T, leaving 19 datasets for training. We did not find performance degradation when removing the single-view datasets. Please refer to the Tab. 6 of the CUT3R for more dataset details.
A.3 MORE IMPLEMENTATION DETAILS
More Training Details. Our method conducts end-to-end training on all datasets on a hybrid of 12 different resolutions, ranging from 224 x 224 to 512 384. Data augmentation side, perform sequence-level color jittering by applying the same color jitter across all frames in a sequence.
Network Architecture Details. We follow DUS13R and use the CroCoNet (Weinzaepfel et al. 2023) pre-trained ViT for the encoder and decoder design. We directly use the DPT (Ranftl et al., 2021 head for Headglobal and Head implementation. We apply RoPE to the query and key Teature before each attention operation for the ViT encoder, but ignore it for the ViT decoder to generalize to an arbitrary number of input views. For ablation studies, we train our model on the same datasets but at resolution 224 x 224.
For the sliding window attention version STREAM3R -W[5], we always include the tokens of the first frame to keep the canonical coordinate space unchanged. We set window size W= 5 since it trades off performance and speed, and other window size also stably works. For the full attention version STREA 9 9-FA, we directly use the causally trained model STREAM3R and remove the causal mask in the SelfAttn. This is similar to the "revisit" operation in CUT3R.
A.4 MORE COMPARISONS AND ANALYSIS
Video Depth Estimation. We further expand the video depth comparison in the main paper and include a wider range of baseline methods, including single-frame depth methods Marigold (Ke V2
et al. 2024) and DepthAnything- V2 (Yang et al. (202cc), video depth approaches NVDS (Wang
al. 2023), DepthCrafter (Hu al. (025). and ChronoDepth (Shao et al., 2024), and recent joint
depth-and-pan estimation methods such as Robust-CVD (Bärsan et 2018). CausalSAM (Zhang ct al. 2022), DUS13R (Wang et al.) 2024d), MASt3R (Leroy et al. 2024). MonST3R (Zhang et al.,
2024a), and Spann3R (Wang& & Agapito 2024). Extended results are shown in Tab. 6 STREAM3R* consistently outperforms its RNN-based counterpart CUT3R under the per-sequence scale shift setting, and even achieves state-of-the performance the KITTI dataset while also being the fastest in terms of FPS. Moreover, STREAM3R delivers even stronger results, attaining the best overall accuracy across the per-sequence scale & shift setting.
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Table 6: Video depth evaluation. We report beaeksssit--ivvriill depth, scale-invariant depth and metric depth accuracy on Sintel, Bonn, and KITTI datasets. Methods requiring global alignment are marked "GA", while "Optim" and "Stream" indicate Optinzation-base and Streamne methods, respectively. We also report the FPS on KITTI dataset using 512x 144 image resolution for all methods, except Spann3R which Stream supports 224 inputs.
Sintel
BONN
KITTI
Alignment
Type
Rel
6x:125
Abs 4 6c1.22
Rel
Marigold [ee
2024
Stream
0.532
$1.5
0.091
93.1
0.149
79.6
<0.1
Depth-Anilnisee
0.367
NVDS Wang ct al 2024
Stream
0.408
48.3
0.167
76.6
0.253
58.8
ChronoDapii
2024
Stream
0.687
48.6
0,100
91.1
0.167
75.9
1,89
DepthCrutter
Stream
0.292
69.7
97.1
0.1
Robust-CV
Kopi al
2021
Stream
0.703
47.8
Per-sequence
CasulSAM
Zhing
1012
Optim
0.387
54.7
0,169
73.7
0.246
62.2
scal & shift
DUSER-GA
Wang al
2023c
Optin
0.531
$1.2
0.156
83.1
0.135
B1.8
0.76
MAS:3--GA
Lawoy et
2024
Optim
0.327
59.4
0.167
78.5
0.137
83.6
0.31
MonS13
ZThang
Optim
0:533
0.35
Spann3R Wand & Apanioo
2124
Stream
0.508
50.8
0.157
82.1
0.2077
73.0
13.55
CUT3R (Wang 20236
Stream
0.540
55.7
0,074
94,5
0.106
88.7
16.58
STREAMER
Stream
0:356
95.7
91.0
STREAM3R
Stream
0.205
70.8
0.062
97.4
0.071
95.1
12.95
DUSBR-GG
Optim
0.656
MASARR-GA
Lawy ai
2124
Optim
0.641
43.9
0.252
70.1
0.183
74.5
0.31
Per-soquence scale
Mo
6134a
Optim
0.378
55.8
0.067
96.3
0.168
74.4
0.35
Spann3R Wang Agupiir
Stream
D.622
81.3
D.
73.7
13:55
Fast3R Yang et ai 2125
FA
0.653
44.9
0,193
77.5
0.140
83.4
47.23
CUTER Wang
0,421
STREAMSK
Stream
0.478
$1.1
0.075
94.1
0.116
89.6
23:48
STREAM3R
Stream
0.264
70.5
0.069
95.2
0,080
94.7
12.95
Metric scale
MASIR GA Lenov et 2024
CUT3R (Wang et al. 20236
Stream
Opti
1.022
1,029
23.8
14.3
0,103
0.272
88.1
70.6
0.122
0.467
85.5
15.2
16.58
0.31
STREAMEK
1.41
0.234
23.48
Table 7: 3D reconstruction comparison on NRGBD (Azinovćć et al. 2022). Our proposed method consistently achieves superior performance compared to optimization-bad based (Optim), streaming- based (Stream), and even full attention (FA) methods. STREAM3R a -FA indicates adopting full attention in our trained model for 3D reconstruction.
Method
Type
Acc
Comp
NCT
Moan
Med.
Mean
Med.
Mean
Med.
VGG-T Wang
2025g
0.073
0.018
0.077
0.021
0,910
0.990
DUSY3R-GA Waing ef mi 21:4
Optim
0.144
0.019
0.154
0.018
0.870
0.982
MASB3R-GA
Optim
0.085
0.033
0.028
0.794
0.928
MonST3R-GA GA
Zhing et al. 2027a
Optim
0.272
0.114
0.287
0.110
0.758
0,843
STREAMER" TA
Stream
0.057
0.028
0.013
0.910
0.993
Spann3R Wang & Arapito 2024
Stream
0.416
0.323
0.417
0,285
0.684
0.789
CUT (waigg
Stream
0.099
0.076
0.026
0.837
0.971
Stream (Zho ct de 2015
Stream
0.084
0.044
0.074
0.041
0.861
0.986
VGG-T [Stremminl Wang
Siream
0.105
0.797
STREAMER
Stream
0.065
0.017
0.034
0.014
0.900
0.991
3D Reconstruction on NRGBD. We further include the comparison on NRGBD benchmark (Azi-
nović et al. 2022) in Tab. 7 Here, we also include the comparison with concurrent work Stream VGGT (Zuuo et al. (025), which fine-tunes VGG-T into streaming version similar to our method. We also include (tr-amntra which indicates using VGG-T in the streaming setting by replace the full attention in VGG-T into the causal attention. As can be seen, our method clearly outperforms all optimization based and online methods, including the official VGG-T model. Di- rect use of VGG-T in the streaming setting substantially degrades performance, underscoring the need for fine-tuning under causal constraints. We also include STRFAM3R for comparison, which indicates replacing the causal attention in STREAM3R into full attention (FA). Interestingly, STREAM3R* $ -FA yields comparable performance compared to VGG-T and even better results on the completion metric. This highlights the effectiveness and generality of our proposed method.
Camera Pose Estimation. Following CUT3R (Wang et al. 2025b), we evaluate camera pose esti- mation accuracy on the Sintel (Butler et al. 2012), TUM-dynamics (Sturm et al.), and ScanNet (Dai jet al. 2017) datasets. Sintel and TUM- -yn-minamics both feature substantial dynamic motion, posing significant challenges conventional SfM and SLAM pipelines. We report Absolute Translation
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Table 8: Camera pose evaluation on Sintel (Butler et al. 2012). TUM-dynamic (Sturm al.), and
ScanNet (Dai et al. 2017) datasets. Our method achieves comparable performance with CUT3R on most benchmarks.
Sintel
TUM-dynamles
ScanNet
Method
Type ATE
RPE trans
RPE
ATE
RPE
RPE rot
ATE
RPE trans
RPE not
Zhao et al.
2022
Optin 0.129
0.031
0.535
0.136
0.023
0.836
Robust-CVD
Kopp
Optim 0.360
0.154
3.443
0.153
0.026
3.528
0.227
0.064
7.374
CasualSAM
Zhang or at.
MUZZ
Optim 0.141
0.035
0.615
0.071
0.010
1.712
0.158
0.034
1.618
DUSER GA
Optim 0.417
0.250
5.796
0.083
0.017
3.567
0.081
0.028
0.784
MASI3R-6G GA
Leboy et
2124
Optim 0.185
0.050
1.496
0
0.012
0.448
0.078
0.020
0.475
MonST3R-GA
3l.
20244
Optim 0.111
0.044
0.869
0.098
0.019
0.935
0.077
0,018
0.529
DUSGR Wane el
2024
Stream 0,290
0.132
7.869
0.140
0.106
3.286
0.246
0.108
8.210
Spann3R
Wany
20744
Stream 0.329
0.110
4.471
0.056
0.021
0.591
0.096
0.023
0.661
CUT3K
Wang et al. 20250
Stream 0,213
0.066
0.621
0.046
0.015
0.473
0.099
0.022
0.600
STREAM3R
Stream 0.213
0.013
Error (ATE), Relative Translation Error (RPFarams). and Relative Rotation Error (RPF after Sim(3)
alignment with the ground truth, following the protocol in (Teed & Deng. [021] et al. [202aa] Wang et al.] 2025b). Our approach operates without requiring camera calibration, similar to the compared baselines (Teed & Deng, 2021). While many prior methods (Kopf et al. 2021 Zhang (et al., 2022) address this via test-time optimization, which jointly estimates intrinsics and dense depth for each sequence. We focus purely online processing. Tab. [r results for Streaming (Stream) and Optimization (Optim) categories, with DUSt3R (Wang et al. 2024d) included in the latter (aligning all frames to the first frame without global alignment). Although optimization systems still achieve the lowest errors overall, our method establishes the strongest performance among streaming approaches, and notably surpasses CUT3R (Wang et al. 2025b) on both TUM- dynamics and ScanNet, demonstrating particular robustness in dynamic environments.
3D Reconstruction on ETH3D. To further verify performance on large-scale data with longer se- quences, we include 3D reconstruction experiments on the ETH3D (Schöps et al. 2017) dataset, as shown in Tab. 9 As can be seen, global aligmment (GA)-based methods (DUSB3R, MASt3R) perform significantly worse than feed-forward reconstruction methods (CUT3R and Ours), indicat- ing that they struggle to generalize to challenging scenes and long video sequences. Furthermore, our method significantly outperforms other streaming approaches (CUT3R, Spann3R, SLAM3R). While the full-attention offline method VGGT performs strongly, our streaming method achieves the best Completeness score among all methods (0.245 vs. VGGT 0.305) and remains competitive in accuracy.
Table 9: 3D Reconstruction Comparison on ETH3D (Schöps et al. 2017 Our proposed method achieves competitive performance compared to optimization- (Optim), streaming- based (Stream), and full attention (FA) methods.
Methud
Type
Accl
Comp
NC1
Mean
Med.
Mean
Med.
Mean
Med.
DUSBR-GA Wang
2024d
Optim
2.582
2.034
2.126
1.544
0.548
0.573
MASER-GA Levv al
2024
Optim
2.682
2,458
2,206
1.734
0.531
0,540
Fast3R Yang
FA
0.832
0.691
0.978
0.683
0.667
0.766
VGG-T
Wang et al
20.34
FA
0.280
0.185
0.305
0.182
0.853
0.950
CUT3R Wane at 2025b
Stream
0.617
0.525
0.747
0.579
0.754
0.848
Spann3R Wang & Agapo
2024
Stream
1,730
1.107
1.373
0.742
0.545
0.634
SLAM3K (Luu ot al
2024
Stream
1.678
1,288
0.996
0.499
0.615
0.681
STREAM3R
Stream
0.363
0.227
0.245
0.094
0.812
0.943
Comparison with VGGT-SLAM. We compare our method with VGGT-SLAM (Maggio et al., 2025) on both static scenes (NRGBD) and dynamic scenes (Sintel and TUM-dynamics), As shown Tab. our approach performs par with SLAM-specialized techniques for static scene construction. Moreover, Tab. 12 and Fig. 6 demonstrates that our method can robustly reconstruct dynamic scenes, capability that conventional SLAM- methods typically lack.
We further emphasize that our method targets a different problem setting from SLAM-based ap- proaches. Our goal is to develop unified streaming 3D/4D reconstruction pipeline capable of han- dling both (dynamic) foreground and background regions, whereas SLAM-bas based methods primarily focus on reconstructing static backgrounds and estimating accurate camera poses.
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Published as a conference paper at ICLR 2026
Despite these differing objectives, approach is fully compatible with ferd-forward SLAM sys- tems and can be seamlessly integrated into their pipelines. As demonstrated in recent work SLAM- Former (Yuan et al. 2025), streaming -aseed 3D reconstruction with KV caching effectively support frontend tasks such as keyframe selection, tracking, and mapping within SLAM system.
0.4
Trah
0.0
SoaIe Vor
-000
-0.08
nator
230
Grond
yout
VGGT-SLAM
Ours
Figure 6: Visualizations of camera pose prediction on the dynamic Sintel dataset compared with VGGT-SLAM. As shown, our method demonstrates robustness dynamic view reconstruction where static view consistency is not maintained, highlighting capability that conventional SLAM- based methods typically lack.
(a) Unordered Input
(b) Non-Overlapping Anchor View
Figure 7: Visualizations of input permutation and non-overlapping anchoring views. (a) STREAM3R maintains high accuracy under unordered input sequences. (b) STREAM3R success- fully reconstructs the scene even when the anchoring view has no overlap with the rest of the se- quence.
Table 10: Impact of first-view degradation on 3D reconstruction (7-Scenes). We compare the ro- bustness of different methods against input degradation. The values in parentheses indicate the performance drop compared to the clean setting, marked in red.
Method
Acc (Mean)
Acc (Med.)
Comp (Mean)
Comp (Med.)
NC (Mean) t
NC (Med.)
VGGT Wang al. 2025g
0.087
0.039
0.09]
0.039
0.787
0.890
CUT3R Wang et 202N6
0.126
0.047
0.154
0.031
0.727
0.834
STREAM3R
0.122
0.044
0,101
0.038
0.746
0.856
VGGT (w/ view deg.)
0.144 (+0.057)
(+0.023)
(+0.081)
(+0.021)
(-0.099)
(-0.079)
CUT3R (w/ 1st view deg.)
0.335 (40.209)
0.270 (+0.223)
0.320 (40.166)
0.276 (40.245)
0.666 (-0.061)
0,752 (-0.082)
STREAM3R2 (w/ view deg.)
0,223 (+0.101)
0.117 (+0.073)
0.214 (+0.113)
0.139 (+0.101)
0.695 (-0.051)
0.789 (-0.067)
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Published as a conference paper at ICLR 2026
Table 11: 3D reconstruction comparison on dense NRGBD (~ (~150 frames). Our method achieves comparable performance to SLAM-baue -aseedd methods on static scenes.
Accp
Compl
NCT
Method
Type
Mean
Med.
Mean
Med.
Menn
Med.
VGGT-SLAM
Magsio
SLAM-based
0,039
0.028
0.009
0,939 0,781 0.756
STREAM3R"
Stream
0: 0.046
0.020
0.012
0,008
0.923
Table 12: Camera pose comparison with VGGT-SLAM on Sintel (Butler et al., 2012) and TUM- dynamics (Sturm al.). Compared to VGGT-SLAM, which focuses on static scene reconstruction, STREAM3R shows robust camera estimation performance on dynamic scenarios.
Sintel
TUM-dynamies
Method
Type
ATE L
RPE trans
RPE rot J
ATE 4
RPE trans
RPE rot -
VGGT-SLAM
SLAM-based
0.305
0.082
4.140
0.041
0.014
0.879
STREAM3R*
Stream
0.213
0.076
0.868
0.026
0.013
0.330
Comparison Local and Global Point Map Prediction. Our method supports both world-point maps and local-point maps (from depth and intrinsics). By using the extrinsics predicted by the camera head, the local-point map can be further projected into the global coordinate frame. As shown in Tab. 1 the point cloud projected from the local stream achieves better performance than direct world-poin prediction. This observation shows the advantage of decomposing the challeng- ing task of global-point map estimation into simpler subproblems. This finding is consistent with insights reported in VGGT and MapAnything (Keetha et al. 2025).
Table 13: Comparison of direct world point prediction and local depth projection on ETH3D.
Method
Acc (Mean)
Acc (Med.)
Comp (Meun)
Comp (Med.)
NC (Mean)
NC (Med.)
Ours (from Local)
0.363 0.449
0.227 0.215
0.245 0,280
0.094
0.812
0.943
Ours (from Global)
0.131
0.809
0.929
More Analysis Window Attention. Here provide additional analysis of the window window-attention attention configuration of STREAM3R- (with window sizes 4/8/ 1/8/16/32/64) on the NRGBD dataset with long sequences. As shown in Table 14 we find a positive correlation between attention window size and 3D reconstruction quality. This exposes controllable trade-off between reconstruction quality and memory usage, allowing users adapt the model their specific hardware constraints.
Table 14: 3D reconstruction comparison on dense NRGBD (~150 views) with different window size. The memory is reported peak memory in GB.
Method
Ace (Meun)
Ace (Med.)
Comp (Meun)
Comp (Med.)
NC (Meun)
NC (Med.)
Memory
STREAMSR [W44]
0.074
0.031
0.021
0.011
0.699
0.894
6.00
STREAM3R W[8]
0.075
0.030
0.019
0,010
0.693
0.589
6.65
STREAMER/-WII
0.080
0.033
0.022
0.896
STREAM3R P-W132]
0.069
0.028
0.021
0,010
0.729
0.910
10.58
TTREAM3R-WWICE
0.051
0.023
0.019
0,010
0.737
0.913
15.93
STREAMER-.WWIN
0.048
0.021
0.016
0.009
0.752
0.921
24.51
STREAM3R (Causal)
0.046
0.020
0.012
0.005
0.756
0.923
30,30
Reconstruction Results on Longer Sequences. To further evaluate long-sequence performance, we conduct experiments on NRGBD and 7-Scenes datasets using frame intervals 2, 5, 7, 10, 20, and 40. As demonstrated in Tab. 15 method consistently outperforms the baselines across all frame intervals and datasets, showing robust scalability to varying sequence lengths. We also report long trajectory of approximately 1.5K frames in Tab. 16 As
the performance of STREAM3R-W can be seen, our method achieves substantially better performance than CUT3R on this challenging long-sequence setting, further demonstrating the advantages of our streaming design.
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Published as a conference paper at ICLR 2026
Table 15: 3D reconstruction comparison on longer sequences (NRGBD & 7-Scenes) with different intervals. We report the median metrics. The interval indicates the sampling sparsity, and the ap- proximate number of input views is shown parentheses.
NRGBD
7-Stemes
(Views)
(--35)
(-75)
(~150)
(-210) (-370) (-750)
(-:0))
(~00)
[-125)
(>-10) (~250) (-500)
Accuracy
CUTSR WWnye al. 2025b
0.032
0.042
0.054
0,110
0.179
0.266
0.013
0.013
0.019
0.039
0.087
0,161
Spunn3R Wang
0.074
0.056
SLAMEK Liu at 2029
0.113
0.107
0.109
0.117
0.119
0.113
0.106
0.096
0.100
0.091
0.097
0.111
STREAMTK
0.019
0.019
0.020
0.022
0.025
0.028
0.013
0.012
0.010
0.015
0.021
0.021
Completenesz
Spunn3R Wanng & Agaging 2014
0.033
0.031
0.045
0.041
0.065
0.089
0.048
0.015
0.017
0.041
0.065
SLAMER Lit at 2029
0.046
0.027
0.015
0.021
0.012
0.072
0.053
0.031
0.019
0.015
0.032
0.056
STREAMER
0.008
0.006
0.005
0.009
0.016
0.018
0.012
0.009
0.006
0.009
0.015
0,021
Consistincy (NC)
CUTSR WWan el al 20256
0.913
0.908
0.825
0.726
0.686
0.638
0.806
0.750
0.693
0.602
0.595
0.573
Spunn3R Wonee & Wooiing 2014
0.750
0..224
0.657
0.624
0.611
0.570
0.710
0.699
0.641
0.571
0.518
0.538
St SLA
0.764
0.729
0.686
0.693
0.637
0.625
0.655
0.623
0.590
0.560
0.669
0.576
STREAMER
Table 16: 3D reconstruction comparison on thousands of frames (~1 1.1.5)). Our method demonstrates
superior stability extremely long sequences compared to CUT3R.
Method
(Mean)
(Med.)
Comp (Mean) |
Comp (Med.)
(Mean)
NC (Med.) 1
CUT3R (Wang et al. 2025b STREAMER W6
0.411
0.315
0.224
0.146 0.015
0.544
0.581
0.094
0.039
0.028
0.627
0.716
A.5 LIMITATIONS
Our method comes with some limitations. First, the naïve causal modeling naturally suffers from eтror accumulation and drifting (Zhang & Agrawala, 2025). Some inference strategies can be proposed to alleviate this issue. Second, currently STREAM3R is still a regression model with deterministic outputs. Extending it further into an autoregressive generative model (Chen et al.) 2025 Zhang & Agrawala, (025) shall further unlock a series of downstream applications. Fi- nally, since STREAM3R follows similar design of modern LLMs, more training techniques like MLA (DeepSeek-Al et al. 2024) can be introduced to further boost the training efficiency and per- formance.
23


Reviews:
[{"review_id": "1h3h5raa6M", "reviewer": "Reviewer_P6AK", "summary": "The paper tackles sequential, feed-forward 3D reconstruction, whereas most existing feed-forward models require processing all images at once. The core idea is a causal Transformer in which intra-frame self-attention is followed by inter-frame cross-attention to cached tokens from past frames, enabling online processing with KV-cache efficiency. A windowed streaming mode retains the first frame (to preserve a canonical world frame) plus the most recent context, bounding memory while maintaining global consistency. The model outputs local/global pointmaps with confidences and camera pose, and the authors fine-tune DUSt3R and VGGT backbones on public datasets. Experiments span video depth estimation and 3D reconstruction, comparing against feed-forward baselines and their pose-graph/global-alignment (GA) extensions. Results show that the proposed method delivers higher accuracy, markedly higher throughput, and linear (or constant, in windowed mode) memory in streaming/online settings without test-time global alignment, with gains especially clear on long sequences and under tight memory constraints.", "questions": "- Please refere to the weakness\n- What happens if the canonical first frame is occluded or does not have overlap with latest frames? Can the model re-anchor and attentive to other frames?\n- How the performance is compared with VGGT-SLAM, which is a pose graph alignment based method built upon VGGT.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "### Originality\n\n* To the best of my knowledge, this is among the first works to extend feed-forward 3D reconstruction to sequential processing via a causal transformer, rather than relying on pose-graph/global alignment.\n* While it could use more elaboration, the [reg] token is an interesting mechanism to make the model explicitly aware of the anchor frame. In addition, omitting view embeddings is a novel choice that encourages order-agnostic generalization to different input image orders.\n\n### Quality\n\n* The comparisons against both feed-forward baselines and their global-alignment (GA) extensions are generally comprehensive, covering video depth and 3D reconstruction on short and long sequences.\n* The generality of the approach is validated by combining it with both DUSt3R and VGGT baselines.\n\n### Clarity\n\n* The paper is well organized and motivated. The method is clearly introduced, and the evaluations are easy to follow.\n\n### Significance\n\n* The solution enables sequential reconstruction without pose-graph alignment, making it potentially complementary to pose-graph methods under conditions such as small overlaps.\n* It imports ideas from LLMs into feed-forward 3D reconstruction, which can inspire follow-up research that leverages recent advances beyond just causal transformers.\n* The method is general and composable with other transformer-based feed-forward models, and its efficiency could enable deployment in compute-limited applications such as robotics.", "weaknesses": "- Contribution may be limited: The gains appear to stem primarily from a causal transformer framework rather than components specific to 3D reconstruction. The paper should clarify what is fundamentally new beyond causal masking and standard transformer design, and what is uniquely tailored to 3D reconstruction.\n\n- Anchor design needs more elaboration: The proposed [reg] token is interesting, but there is no controlled comparison to simpler anchors such as a global CLS token or relative view positional embeddings. This makes it hard to judge when [reg] is necessary and sufficient. The paper also lacks experiments on unordered image collections that could demonstrate the hypothesized advantage.\n\n- Experimental scale and fairness: Most experiments rely on small-scale data (e.g., 7-scenes), which weakens claims—especially against pose-graph alignment methods. 7-scenes has strong inter-image connectivity and may not expose long-sequence drift. Since attention couples camera poses and depths, it is unclear whether bundle-adjustment refinement is applied, which affects fairness.\n\n- Missing robustness and failure analysis: The paper offers limited analysis of failure cases to clarify pros/cons relative to global alignment approaches.", "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": "Eg5UIBRgSZ", "reviewer": "Reviewer_JPV9", "summary": "This paper introduces STREAM3R, a novel framework that reformulates sequential 3D reconstruction as a decoder-only causal transformer problem, inspired by autoregrssive-based LLMs. The core contribution is an efficient streaming architecture that processes frames sequentially, using causal attention to integrate geometric context from a growing KVCache of previously observed features. This design enables high scalability for long sequences and efficient inference by leveraging modern LLM-style techniques like windowed attention. The method achieves state-of-the-art or highly competitive performance on numerous 3D reconstruction and video depth benchmarks, outperforming prior streaming and even full-attention-based approaches.", "questions": "1. The primary technical weakness is the inherent risk of error accumulation and drift common to online, sequential methods. The paper does not propose or evaluate explicit mechanisms to combat this (e.g., loop closure, keyframe-based optimization) and it is unclear how the system would perform on extremely long sequences (e.g., thousands of frames) beyond what was tested.\n\n2. The global coordinate system is anchored to the very first frame using a learnable [reg] token. The robustness of this simple mechanism is not ablated or discussed. It is unclear how the system would perform if the initial frame is of poor quality (e.g., blurry, featureless, or heavily occluded), which could compromise the entire reconstruction.\n\n3. A key claim is the compatibility with \"LLM-style training infrastructure,\" but the experiments primarily validate LLM-style inference techniques (KVCache, windowed attention). The paper does not include experiments applying advanced LLM-style training optimizations (e.g., specific curriculum strategies, advanced data parallelism, or training-focused attention mechanisms, which the authors themselves allude to in the limitations). This makes the claim about training benefits less substantiated than the clear inference benefits, and complementary experiments would strengthen this claim.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The core architectural proposal to use a decoder-only causal transformer with a KVCache is a novel and highly effective paradigm for streaming 3D reconstruction. It offers a more scalable and powerful alternative to prior methods based on RNNs-like structure (like CUT3R) or expensive global optimization (like VGG-T or DUSt3R-GA).\n\n2. The method demonstrates sota competitive performance across a comprehensive suite of benchmarks, including video depth estimation, 3D reconstruction, and camera pose estimation. It consistently and significantly outperforms its most direct streaming competitor, CUT3R.\n\n3. The paper provides a thorough analysis of computational trade-offs. The STREAM3R-V-W[5] (windowed) variant is particularly noteworthy, achieving the fastest inference speed (32.9 FPS) among all streaming methods while simultaneously delivering top-tier reconstruction accuracy, demonstrating the practical viability of the approach. The memory usage analysis in Table 4 clearly validates the scalability claims.", "weaknesses": "please refer to the weakness part.", "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": "ipU2RGDEPf", "reviewer": "Reviewer_vTS9", "summary": "Conducting feed-forward 3D reconstruction from monocular videos is nowadays a very hot topic and a series of works followed dust3R and VGGT. This work is an extension of VGGT to make reconstruction be able to support streaming input. The goal is reached by reformulating the traditional global attention of traditional vggt framework into a sequential attention mechanism. Experiments are sufficient to verify the superior performance of the proposed framework.", "questions": "No more.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The target problem to support feed-forward and streaming 3D reconstruction is timely, the proposed framework also makes sense. Assume the review does not need to consider concurrent works, I think the proposed framework is compatable with existing concurrent works in terms of both performance and novelty.", "weaknesses": "I only have some minor concerns:\n- As shown in table 7, StreamVGGT is attending the comparison which is a concurrent work with similiar key idea. From the results, it seems the proposed method outperforms StreamVGGT, but with no any explanations. I understand this work is a concurrent work. However, the explanation about the differences bettween streamVGGT and the proposed method and why the results of the proposed method are better is helpful for understanding. \n\n- In the contribution list, there are 2 points are highlighted: 1) compatible with LLM-style training, allowding efficient and scalable context accumulation across frames; 2) supports both world- and local- pointmap and natually generallizes to large-scale novel view synthesis. However, there is no any experiments and discussions to support this.", "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": "dEgmQWzK0O", "reviewer": "Reviewer_shrJ", "summary": "This paper introduces a streaming version of VGGT, which can process image sequences efficiently using causal attention.\nThe authors conduct extensive experiments to show the effectiveness and advantages of their method on various tasks.", "questions": "Please refer to the Weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper is well-written and easy to follow. It also provides a nice demo to show their performance.\n2. The authors conduct extensive experiments to show the effectiveness of their method.", "weaknesses": "1. The backbone is highly related to that of VGGT, and the usage of causal attention do not have enough contribution considering the high computational cost when the input sequences are very long (same with VGGT).\n2. I think that since the authors highlighted the advantage of Stream3R-W in Table 4 when analyzing memory usage, its results should also be included in other performance experiments (like reconstruction) for comparison. After all, the memory usage of Stream3R without the window mechanism gradually exceeds that of CUT3R (also a streaming-based method). I believe that a comprehensive analysis of Stream3R and Stream3R-W would help provide a better evaluation of this paper.\n3. Reconstruction results on longer sequences. The sequences used in Table 3 are too short, which is not enough to evaluate a streaming-based method. Please provide more comparison with Spann3R, SLAM3R, and CUT3R on sequences at the intervals of 2, 10, 20, 40 (7-scenes and NRGBD).\n4. Please provide reconstruction results comparison on ETH3D datasets, like VGGT.\n\nPlease answer the above questions carefully and provide more thorough discussion and comparison. I will consider raising the score.", "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)