GLADOS reconstructs 3D geometry from disjoint views by generating intermediate perspectives, performing robust coarse alignment that tolerates generative inconsistencies, and iteratively expanding context for consistency.
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Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs
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In this paper, we introduce Splatt3R, a pose-free, feed-forward method for in-the-wild 3D reconstruction and novel view synthesis from stereo pairs. Given uncalibrated natural images, Splatt3R can predict 3D Gaussian Splats without requiring any camera parameters or depth information. For generalizability, we build Splatt3R upon a ``foundation'' 3D geometry reconstruction method, MASt3R, by extending it to deal with both 3D structure and appearance. Specifically, unlike the original MASt3R which reconstructs only 3D point clouds, we predict the additional Gaussian attributes required to construct a Gaussian primitive for each point. Hence, unlike other novel view synthesis methods, Splatt3R is first trained by optimizing the 3D point cloud's geometry loss, and then a novel view synthesis objective. By doing this, we avoid the local minima present in training 3D Gaussian Splats from stereo views. We also propose a novel loss masking strategy that we empirically find is critical for strong performance on extrapolated viewpoints. We train Splatt3R on the ScanNet++ dataset and demonstrate excellent generalisation to uncalibrated, in-the-wild images. Splatt3R can reconstruct scenes at 4FPS at 512 x 512 resolution, and the resultant splats can be rendered in real-time.
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ZipSplat uses multi-view token extraction followed by k-means clustering and attention to decode compact scene tokens into unconstrained 3D Gaussians, achieving SOTA pose-free results with ~6x fewer primitives.
ArtSplat is the first feed-forward framework for articulated 3D Gaussian Splatting that reconstructs geometry and joints from sparse multi-state uncalibrated views in one pass.
NoPo4D is the first feed-forward system for dynamic 4D Gaussian splatting from unposed multi-view videos, using velocity decomposition supervised by optical flow and a bidirectional motion encoder.
ConFixGS repairs feedforward 3D Gaussian Splatting with confidence-aware diffusion priors, delivering up to 3.68 dB PSNR gains and halved FID scores on Waymo, nuScenes, and KITTI novel view synthesis tasks.
SplatWeaver uses cardinality Gaussian experts and pixel-level routing to dynamically allocate varying numbers of Gaussian primitives for generalizable novel view synthesis.
Ground4D resolves temporal conflicts in feedforward 4D Gaussian reconstruction for off-road scenes via voxel-grounded temporal aggregation with intra-voxel softmax and surface normal regularization, outperforming prior methods on ORAD-3D and RELLIS-3D while generalizing zero-shot.
WildSplatter jointly learns 3D Gaussians and appearance embeddings from unconstrained photo collections to enable fast feed-forward reconstruction and flexible lighting control in 3D Gaussian Splatting.
Free-Range Gaussians uses flow matching over Gaussian parameters to predict non-grid-aligned 3D Gaussians from multi-view images, enabling synthesis of plausible content in unobserved regions with fewer primitives than grid-aligned methods.
3AM integrates MUSt3R 3D features into SAM2 via a Feature Merger and FOV-aware sampling to deliver geometry-consistent video object segmentation from RGB alone, with large gains on wide-baseline datasets.
VGGT-SLAM aligns VGGT submaps via SL(4) manifold optimization of 15-DoF homographies to enable consistent dense RGB SLAM on long uncalibrated monocular videos.
Diversity-aware graph partitioning of views into balanced chunks, using visual dissimilarity plus soft pose propagation, makes VGGT scale to long unordered sequences with better accuracy and lower cost.
COVScene is a pose-free framework that lifts semantic Gaussians into a volumetric occupancy field during training to jointly support novel view synthesis, open-vocabulary segmentation, and semantic occupancy prediction.
A feed-forward framework learns instance-structured 3D token groups from unposed multi-view images via differentiable rendering, enabling native object-level segmentation, editing, and retrieval without 3D supervision.
StructSplat introduces a structured 3D Gaussian splatting framework that performs feed-forward reconstruction from uncalibrated sparse views using pixel-aligned features, semantic priors, and camera alignment.
Error-Conditioned Neural Solvers improve PDE prediction accuracy by using the residual field as network input for learned corrections, outperforming residual-minimization methods by up to 10x on turbulent flows and generalizing better under distribution shifts.
Wild3R is a feed-forward 3D Gaussian Splatting model trained on the new WildCity dataset of 200 scenes with 170 lighting conditions and transients to handle unconstrained sparse photo collections.
Robust Dreamer uses Latent Gaussian Memory anchored to diffusion latents and Deviation Learning with a Dynamic Deviation Archive to reduce drift in long-horizon action-controlled image-to-video generation, reporting SOTA results on ScanNet, DL3DV, and OmniWorldGame.
TriSplat predicts oriented triangle primitives from images in one forward pass to produce simulation-ready 3D meshes with competitive rendering quality.
A feed-forward model aligns ground and satellite features to predict Gaussian splats for improved novel-view synthesis on georeferenced outdoor scenes.
DeG models 3D Gaussians via learned octree density and uses VecSeq Sobol re-indexing to turn set generation into sequence modeling, claiming SOTA quality in single-image-to-3D.
FluSplat trains a model with geometric alignment constraints on multi-view edits to produce consistent 3D scene edits from sparse views in a single forward pass without test-time optimization.
LingBot-Map is a streaming 3D reconstruction model built on a geometric context transformer that combines anchor context, pose-reference window, and trajectory memory to deliver accurate, drift-resistant results at 20 FPS over sequences longer than 10,000 frames.
The paper proposes a problem-driven taxonomy for feed-forward 3D scene modeling that groups methods by five core challenges: feature enhancement, geometry awareness, model efficiency, augmentation strategies, and temporal-aware modeling.
citing papers explorer
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Mind the Gap: Geometrically Accurate Generative Reconstruction from Disjoint Views
GLADOS reconstructs 3D geometry from disjoint views by generating intermediate perspectives, performing robust coarse alignment that tolerates generative inconsistencies, and iteratively expanding context for consistency.
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ZipSplat: Fewer Gaussians, Better Splats
ZipSplat uses multi-view token extraction followed by k-means clustering and attention to decode compact scene tokens into unconstrained 3D Gaussians, achieving SOTA pose-free results with ~6x fewer primitives.
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ArtSplat: Feed-Forward Articulated 3D Gaussian Splatting from Sparse Multi-State Uncalibrated Views
ArtSplat is the first feed-forward framework for articulated 3D Gaussian Splatting that reconstructs geometry and joints from sparse multi-state uncalibrated views in one pass.
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No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos
NoPo4D is the first feed-forward system for dynamic 4D Gaussian splatting from unposed multi-view videos, using velocity decomposition supervised by optical flow and a bidirectional motion encoder.
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ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes
ConFixGS repairs feedforward 3D Gaussian Splatting with confidence-aware diffusion priors, delivering up to 3.68 dB PSNR gains and halved FID scores on Waymo, nuScenes, and KITTI novel view synthesis tasks.
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SplatWeaver: Learning to Allocate Gaussian Primitives for Generalizable Novel View Synthesis
SplatWeaver uses cardinality Gaussian experts and pixel-level routing to dynamically allocate varying numbers of Gaussian primitives for generalizable novel view synthesis.
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Ground4D: Spatially-Grounded Feedforward 4D Reconstruction for Unstructured Off-Road Scenes
Ground4D resolves temporal conflicts in feedforward 4D Gaussian reconstruction for off-road scenes via voxel-grounded temporal aggregation with intra-voxel softmax and surface normal regularization, outperforming prior methods on ORAD-3D and RELLIS-3D while generalizing zero-shot.
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WildSplatter: Feed-forward 3D Gaussian Splatting with Appearance Control from Unconstrained Images
WildSplatter jointly learns 3D Gaussians and appearance embeddings from unconstrained photo collections to enable fast feed-forward reconstruction and flexible lighting control in 3D Gaussian Splatting.
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Free-Range Gaussians: Non-Grid-Aligned Generative 3D Gaussian Reconstruction
Free-Range Gaussians uses flow matching over Gaussian parameters to predict non-grid-aligned 3D Gaussians from multi-view images, enabling synthesis of plausible content in unobserved regions with fewer primitives than grid-aligned methods.
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3AM: 3egment Anything with Geometric Consistency in Videos
3AM integrates MUSt3R 3D features into SAM2 via a Feature Merger and FOV-aware sampling to deliver geometry-consistent video object segmentation from RGB alone, with large gains on wide-baseline datasets.
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VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold
VGGT-SLAM aligns VGGT submaps via SL(4) manifold optimization of 15-DoF homographies to enable consistent dense RGB SLAM on long uncalibrated monocular videos.
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Diversity-aware View Partitioning for Scalable VGGT
Diversity-aware graph partitioning of views into balanced chunks, using visual dissimilarity plus soft pose propagation, makes VGGT scale to long unordered sequences with better accuracy and lower cost.
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Bridging 3D Gaussians and Semantic Occupancy for Comprehensive Open-Vocabulary Scene Understanding from Unposed Images
COVScene is a pose-free framework that lifts semantic Gaussians into a volumetric occupancy field during training to jointly support novel view synthesis, open-vocabulary segmentation, and semantic occupancy prediction.
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Scenes as Objects, Not Primitives: Instance-Structured 3D Tokenization from Unposed Views
A feed-forward framework learns instance-structured 3D token groups from unposed multi-view images via differentiable rendering, enabling native object-level segmentation, editing, and retrieval without 3D supervision.
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StructSplat: Generalizable 3D Gaussian Splatting from Uncalibrated Sparse Views
StructSplat introduces a structured 3D Gaussian splatting framework that performs feed-forward reconstruction from uncalibrated sparse views using pixel-aligned features, semantic priors, and camera alignment.
-
Error-Conditioned Neural Solvers
Error-Conditioned Neural Solvers improve PDE prediction accuracy by using the residual field as network input for learned corrections, outperforming residual-minimization methods by up to 10x on turbulent flows and generalizing better under distribution shifts.
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Wild3R: Feed-Forward 3D Gaussian Splatting from Unconstrained Sparse Photo Collection
Wild3R is a feed-forward 3D Gaussian Splatting model trained on the new WildCity dataset of 200 scenes with 170 lighting conditions and transients to handle unconstrained sparse photo collections.
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Robust Dreamer: Deviation-Aware Latent Gaussian Memory for Action-Controlled AR Video Generation
Robust Dreamer uses Latent Gaussian Memory anchored to diffusion latents and Deviation Learning with a Dynamic Deviation Archive to reduce drift in long-horizon action-controlled image-to-video generation, reporting SOTA results on ScanNet, DL3DV, and OmniWorldGame.
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TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction
TriSplat predicts oriented triangle primitives from images in one forward pass to produce simulation-ready 3D meshes with competitive rendering quality.
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Cross-View Splatter: Feed-Forward View Synthesis with Georeferenced Images
A feed-forward model aligns ground and satellite features to predict Gaussian splats for improved novel-view synthesis on georeferenced outdoor scenes.
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Generative 3D Gaussians with Learned Density Control
DeG models 3D Gaussians via learned octree density and uses VecSeq Sobol re-indexing to turn set generation into sequence modeling, claiming SOTA quality in single-image-to-3D.
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FluSplat: Sparse-View 3D Editing without Test-Time Optimization
FluSplat trains a model with geometric alignment constraints on multi-view edits to produce consistent 3D scene edits from sparse views in a single forward pass without test-time optimization.
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Geometric Context Transformer for Streaming 3D Reconstruction
LingBot-Map is a streaming 3D reconstruction model built on a geometric context transformer that combines anchor context, pose-reference window, and trajectory memory to deliver accurate, drift-resistant results at 20 FPS over sequences longer than 10,000 frames.
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Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective
The paper proposes a problem-driven taxonomy for feed-forward 3D scene modeling that groups methods by five core challenges: feature enhancement, geometry awareness, model efficiency, augmentation strategies, and temporal-aware modeling.
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LiveStre4m: Feed-Forward Live Streaming of Novel Views from Unposed Multi-View Video
LiveStre4m delivers real-time novel-view video streaming from unposed multi-view inputs via a multi-view vision transformer, diffusion-transformer interpolation, and a learned camera pose predictor.
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DePT3R: Joint Dense Point Tracking and 3D Reconstruction of Dynamic Scenes in a Single Forward Pass
DePT3R performs joint dense point tracking and 3D reconstruction of dynamic scenes from multiple unposed images using a single neural network forward pass.
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C3G: Learning Compact 3D Representations with 2K Gaussians
C3G creates compact 3D Gaussian representations with 2K points by guiding placement via learnable tokens that aggregate multi-view features through attention, yielding better efficiency and performance than dense methods.
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Depth Anything 3: Recovering the Visual Space from Any Views
DA3 recovers consistent visual geometry from arbitrary views via a vanilla DINO transformer and depth-ray target, setting new SOTA on a visual geometry benchmark while outperforming DA2 on monocular depth.
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Densemarks: Learning Canonical Embeddings for Human Heads Images via Point Tracks
DenseMarks learns a canonical 3D embedding space for human head images by training a Vision Transformer with contrastive loss on pairwise point tracks from in-the-wild videos, plus landmark and segmentation supervision.
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LTGS: Long-Term Gaussian Scene Chronology From Sparse View Updates
LTGS uses object template Gaussians as reusable priors that are refined via a pipeline to model long-term scene chronology from sparse-view updates in 3D Gaussian Splatting.
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Streaming 4D Visual Geometry Transformer
A causal transformer with key-value caching and distillation from a bidirectional VGGT model enables efficient online 4D geometry reconstruction from videos.
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Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Geometry Forcing aligns video diffusion representations with geometric foundation model features via angular cosine and scale regression objectives to improve 3D consistency in generated videos.
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The Less You Depend, The More You Learn: Synthesizing Novel Views from Sparse, Unposed Images with Minimal 3D Knowledge
Data-centric novel view synthesis models with minimal 3D knowledge and no pose annotations scale better with data volume and outperform traditional bias-driven methods.
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L2D2-GS: Learning to Densify for Feedforward Dynamic Gaussian Scene Reconstruction
L2D2-GS reformulates generalizable dynamic Gaussian reconstruction as iterative optimization with a self-supervised densification policy and geometric regularization, claiming SOTA fidelity and zero-shot generalization on PandaSet and Waymo with fewer primitives.
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Empowering Feed-Forward Reconstruction Models with Metric Scale via Satellite Images
Satellite imagery is integrated via cross-view attention into feed-forward 3D reconstruction to resolve global scale ambiguity and produce metric outputs.
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DVSM: Decoder-only View Synthesis Model Done Right
Decoder-only view synthesis model using KV-cache representation and weight sharing between reconstruction and rendering networks achieves new SOTA on novel view synthesis benchmarks.
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LangFlash: Feed-forward 3D Language Gaussian Splatting from Sparse Unposed Images
LangFlash introduces a feed-forward model for 3D language Gaussian splatting from sparse unposed images, claiming superior novel view synthesis and semantic consistency via enriched training data and sparse semantic encoding.
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ReorgGS: Equivalent Distribution Reorganization for 3D Gaussian Splatting
ReorgGS reorganizes the Gaussian distribution in converged 3DGS models by resampling centers and covariances to reduce parameterization degeneration and enable better subsequent optimization.
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Learning 3D Representations for Spatial Intelligence from Unposed Multi-View Images
UniSplat learns consistent 3D geometry, appearance, and semantics from unposed images using dual masking, progressive Gaussian splatting, and recalibration to align predictions across tasks.
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VGGT-SLAM++
VGGT-SLAM++ improves on prior transformer SLAM by adding dense DEM submap graphs and high-cadence local optimization, achieving SOTA accuracy with reduced drift and bounded memory on benchmarks.
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