Geo-Align applies RL with a perceptual reward derived from 3D camera trajectory estimation to improve controllability and fidelity in video generation without paired training data.
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Grounding image matching in 3d with mast3r
21 Pith papers cite this work. Polarity classification is still indexing.
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GenRecon lifts object-level generative priors to scene-scale reconstruction by chunking scenes and using projection-based conditioning on multi-view features, claiming 16% better results than prior methods.
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.
EvoScene-VLA maintains an action-updated scene prior across control chunks in VLA policies, raising success rates on RoboTwin tasks from 87.2% to 89.1% fixed and 86.1% to 88.5% randomized while outperforming baselines on a real robot.
CRePE supplies depth-aware positional distributions along curved rays for stable unified-camera control in frozen video DiT models.
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.
A new benchmark with real lunar stereo ground truth and analog data shows that sim-to-real fine-tuned monocular depth models achieve large in-domain gains but minimal generalization to actual lunar images.
Affostruction reconstructs full 3D object geometry from partial RGBD views and grounds text-based affordances on both visible and unobserved surfaces, reporting large gains over prior methods.
FastForward represents scenes as collections of 3D-anchored image features and performs camera pose estimation via feed-forward correspondence prediction, achieving competitive accuracy with minimal mapping time.
SADGE is a new fused similarity metric combining DINOv3 appearance and MASt3R geometry via constrained bilinear interaction that correlates with downstream synthetic-to-real performance at Pearson r=0.88 across multiple benchmarks.
SpaceMind++ adds an explicit voxelized allocentric cognitive map and coordinate-guided fusion to video MLLMs, claiming SOTA on VSI-Bench and improved out-of-distribution generalization on three other 3D benchmarks.
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.
Reframing head pose estimation as relative pose prediction between image pairs enables a synthetic-only trained model to outperform absolute regression methods on real benchmarks.
A feed-forward video latent transformer that predicts time-varying 3D Gaussian primitives from one image to produce controllable 4D scenes with appearance, geometry, and motion.
A causal transformer with key-value caching and distillation from a bidirectional VGGT model enables efficient online 4D geometry reconstruction from videos.
RoDyGS separates static and dynamic elements in monocular videos using Gaussian splatting with regularization and introduces the Kubric-MRig benchmark for pose-free dynamic novel view synthesis.
Introduces an eager-mode PyTorch BA library with GPU-accelerated sparse ops claiming 18.5-23x speedups over GTSAM, g2o, and Ceres.
ViewCrafter tames video diffusion models with point-based 3D guidance and iterative trajectory planning to produce high-fidelity novel views from single or sparse images.
PatchPoison injects 12x12 pixel checkerboard patches into multi-view images to disrupt SfM feature matching, causing 3DGS reconstructions to diverge with 6.8x higher LPIPS error on NeRF-Synthetic while remaining unobtrusive.
UniDepthV2 predicts metric 3D points directly from single images using a self-promptable camera module, pseudo-spherical representation, and new losses for improved cross-domain generalization.
Splatt3R is a feed-forward network that predicts 3D Gaussian splats directly from uncalibrated stereo image pairs by extending MASt3R with appearance attributes and a two-stage training procedure.
citing papers explorer
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Geo-Align: Video Generation Alignment via Metric Geometry Reward
Geo-Align applies RL with a perceptual reward derived from 3D camera trajectory estimation to improve controllability and fidelity in video generation without paired training data.
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GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction
GenRecon lifts object-level generative priors to scene-scale reconstruction by chunking scenes and using projection-based conditioning on multi-view features, claiming 16% better results than prior methods.
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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.
-
EvoScene-VLA: Evolving Scene Beliefs Inside the Action Decoder for Chunked Robot Control
EvoScene-VLA maintains an action-updated scene prior across control chunks in VLA policies, raising success rates on RoboTwin tasks from 87.2% to 89.1% fixed and 86.1% to 88.5% randomized while outperforming baselines on a real robot.
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CRePE: Curved Ray Expectation Positional Encoding for Unified-Camera-Controlled Video Generation
CRePE supplies depth-aware positional distributions along curved rays for stable unified-camera control in frozen video DiT models.
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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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LuMon: A Comprehensive Benchmark and Development Suite with Novel Datasets for Lunar Monocular Depth Estimation
A new benchmark with real lunar stereo ground truth and analog data shows that sim-to-real fine-tuned monocular depth models achieve large in-domain gains but minimal generalization to actual lunar images.
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Affostruction: 3D Affordance Grounding with Generative Reconstruction
Affostruction reconstructs full 3D object geometry from partial RGBD views and grounds text-based affordances on both visible and unobserved surfaces, reporting large gains over prior methods.
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A Scene is Worth a Thousand Features: Feed-Forward Camera Localization from a Collection of Image Features
FastForward represents scenes as collections of 3D-anchored image features and performs camera pose estimation via feed-forward correspondence prediction, achieving competitive accuracy with minimal mapping time.
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SADGE: Structure and Appearance Domain Gap Estimation of Synthetic and Real Data
SADGE is a new fused similarity metric combining DINOv3 appearance and MASt3R geometry via constrained bilinear interaction that correlates with downstream synthetic-to-real performance at Pearson r=0.88 across multiple benchmarks.
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SpaceMind++: Toward Allocentric Cognitive Maps for Spatially Grounded Video MLLMs
SpaceMind++ adds an explicit voxelized allocentric cognitive map and coordinate-guided fusion to video MLLMs, claiming SOTA on VSI-Bench and improved out-of-distribution generalization on three other 3D benchmarks.
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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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VGGT-HPE: Reframing Head Pose Estimation as Relative Pose Prediction
Reframing head pose estimation as relative pose prediction between image pairs enables a synthetic-only trained model to outperform absolute regression methods on real benchmarks.
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Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models
A feed-forward video latent transformer that predicts time-varying 3D Gaussian primitives from one image to produce controllable 4D scenes with appearance, geometry, and motion.
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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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RoDyGS: Robust Dynamic Gaussian Splatting for Casual Videos
RoDyGS separates static and dynamic elements in monocular videos using Gaussian splatting with regularization and introduces the Kubric-MRig benchmark for pose-free dynamic novel view synthesis.
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Bundle Adjustment in the Eager Mode
Introduces an eager-mode PyTorch BA library with GPU-accelerated sparse ops claiming 18.5-23x speedups over GTSAM, g2o, and Ceres.
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ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
ViewCrafter tames video diffusion models with point-based 3D guidance and iterative trajectory planning to produce high-fidelity novel views from single or sparse images.
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PatchPoison: Poisoning Multi-View Datasets to Degrade 3D Reconstruction
PatchPoison injects 12x12 pixel checkerboard patches into multi-view images to disrupt SfM feature matching, causing 3DGS reconstructions to diverge with 6.8x higher LPIPS error on NeRF-Synthetic while remaining unobtrusive.
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UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler
UniDepthV2 predicts metric 3D points directly from single images using a self-promptable camera module, pseudo-spherical representation, and new losses for improved cross-domain generalization.
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Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs
Splatt3R is a feed-forward network that predicts 3D Gaussian splats directly from uncalibrated stereo image pairs by extending MASt3R with appearance attributes and a two-stage training procedure.