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Open problems in 3D scene

3D consistency in generated scenes3 papers raise it · 3 address it
Generated videos and scenes often lack geometric and multi-view consistency, breaking immersion and realism.
- OuroWorld: Bringing Any 3D World Alive as Diverse, Endlessly Looping 3D Cinemagraphsraises · addresses
- Orbia: Do Generated Videos Form a Consistent 3D World?raises · addresses
- GeoFix: Geometry Fixing for 4D Consistent Generationraises · addresses
High-quality novel view synthesis under large viewpoint changes3 papers raise it · 3 address it
Feed-forward methods struggle to render high-quality views when camera poses differ significantly from training data.
- InfiniSplat v2: Analyzing and Improving Feed-Forward 3DGS from Any Viewsraises · addresses
- LEGO: A Lifting-Free Approach for Exocentric-to-Egocentric Video Generationraises · addresses
- NAMVIS: Next-Scale Autoregressive Multi-View Image Synthesisraises · addresses
Physics and geometry coherence in 3D scene generation1 paper raises it · 1 addresses it
Generated objects often lack fine-grained spatial and physical compatibility with their surroundings.
- Tetris3D: 3D Scene Generation With Objects That Fit Togetherraises · addresses
Dense correspondence matching beyond rigid motion1 paper raises it · 1 addresses it
Existing methods fail to match dense correspondences when transformations break physical continuity but preserve identity.
- Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matchingraises · addresses
Metric distance estimation in 3D reconstruction1 paper raises it · 1 addresses it
Current benchmarks do not directly evaluate point-to-point distance accuracy in reconstructed 3D scenes.
- Pumpire: Unified Benchmark for Metric Distance Estimationraises · addresses

Named by an AI from these papers' abstracts.

Open problems

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Topics in this field

3d scene · 3d reconstruction · depth estimation250 papers37 this week
gaussian splatting 3dgs · 3d gaussian splatting · feed forward 3d198 papers32 this week
remote sensing · satellite imagery · earth observation123 papers23 this week
small target detection · infrared small target · target detection112 papers21 this week
point cloud registration · 3d point clouds · point cloud63 papers16 this week
camera lidar · 3d object detection · point clouds88 papers9 this week

Topics in this field

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Active contributors

Activity · 12 weeks

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arXiv cs.CVOct 18
MCSplat: Multi-View Photometric and Geometric Consistent Feed-Forward Gaussian Splatting for Driving Scenes
Yifei Wang, Wei Tian, Wenhua Bao, Jia Zhou, Hanshi Wang +8
Driving-scene reconstruction via feed-forward Gaussian splatting from sparse multi-view images remains challenging due to two factors. First, cross-camera photometric inconsistency, caused by variations in exposure, ISP, and illumination, can be incorrectly absorbed into Gaussian geometry. Second, unreliable local geometric supervision arises from normal-based constraints near depth discontinuities. To address these issues, we present \textbf{MCSplat}, a multi-view consistent feed-forward Gaussian splatting framework for driving scenes. MCSplat constructs a canonical appearance space from depth-guided multi-camera correspondences to decouple geometry learning from camera-specific appearance. A multi-scale Bilateral Grid Transformer restores raw camera appearance from canonical renderings, enabling photometric supervision without contaminating Gaussian geometry. To improve local geometric reliability, we estimate Fisher-information-based uncertainty from photometric sensitivity and use it to adaptively weight surface-oriented self-supervision. Extensive experiments demonstrate improved reconstruction quality and generalization across driving datasets. Geometry-oriented evaluations…
PDF arXiv
arXiv cs.CVOct 18
Holo4D: Holistic 4D Reconstruction as Geometric Control for Video Diffusion
Yushi Lan, Zeren Jiang, Koichi Namekata, Xingang Pan, Chuanxia Zheng +1
Synthesizing a monocular video from a new camera trajectory requires preserving the observed scene geometry and motion while completing regions that become newly visible. Feed-forward 4D reconstructors recover the structure of the observed scene, whereas video diffusion models provide a generative prior for completing unseen content. We introduce Holo4D, a video reshooting framework that uses reconstructed 3D trajectories as a holistic source-view 4D scaffold. We project these trajectories into the requested views and encode their geometry and source appearance to guide a pretrained video diffusion model. Together with the source video and target cameras, the scaffold grounds generation in reconstructed observations while leaving unsupported regions to the generative prior. Experiments demonstrate the value of trajectory-based conditioning for camera control and novel-view synthesis. The framework accommodates different 4D reconstructors without retraining the video generator, allowing it to benefit from future advances in source reconstruction. More details can be found in our project page.

Papers

Topics in this field

3d scene · 3d reconstruction · depth estimation250 papers37 this week
gaussian splatting 3dgs · 3d gaussian splatting · feed forward 3d198 papers32 this week
remote sensing · satellite imagery · earth observation123 papers23 this week
small target detection · infrared small target · target detection112 papers21 this week
point cloud registration · 3d point clouds · point cloud63 papers16 this week
camera lidar · 3d object detection · point clouds88 papers9 this week

Active contributors · 90 days

Activity · 12 weeks

Coming with more sources: repos

Open problems in 3D scene

3D consistency in generated scenes3 papers raise it · 3 address it
Generated videos and scenes often lack geometric and multi-view consistency, breaking immersion and realism.
- OuroWorld: Bringing Any 3D World Alive as Diverse, Endlessly Looping 3D Cinemagraphsraises · addresses
- Orbia: Do Generated Videos Form a Consistent 3D World?raises · addresses
- GeoFix: Geometry Fixing for 4D Consistent Generationraises · addresses
High-quality novel view synthesis under large viewpoint changes3 papers raise it · 3 address it
Feed-forward methods struggle to render high-quality views when camera poses differ significantly from training data.
- InfiniSplat v2: Analyzing and Improving Feed-Forward 3DGS from Any Viewsraises · addresses
- LEGO: A Lifting-Free Approach for Exocentric-to-Egocentric Video Generationraises · addresses
- NAMVIS: Next-Scale Autoregressive Multi-View Image Synthesisraises · addresses
Physics and geometry coherence in 3D scene generation1 paper raises it · 1 addresses it
Generated objects often lack fine-grained spatial and physical compatibility with their surroundings.
- Tetris3D: 3D Scene Generation With Objects That Fit Togetherraises · addresses
Dense correspondence matching beyond rigid motion1 paper raises it · 1 addresses it
Existing methods fail to match dense correspondences when transformations break physical continuity but preserve identity.
- Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matchingraises · addresses
Metric distance estimation in 3D reconstruction1 paper raises it · 1 addresses it
Current benchmarks do not directly evaluate point-to-point distance accuracy in reconstructed 3D scenes.
- Pumpire: Unified Benchmark for Metric Distance Estimationraises · addresses

Named by an AI from these papers' abstracts.

Recent papers

All in Research →

arXiv cs.CVOct 18
MCSplat: Multi-View Photometric and Geometric Consistent Feed-Forward Gaussian Splatting for Driving Scenes
Yifei Wang, Wei Tian, Wenhua Bao, Jia Zhou, Hanshi Wang +8
Driving-scene reconstruction via feed-forward Gaussian splatting from sparse multi-view images remains challenging due to two factors. First, cross-camera photometric inconsistency, caused by variations in exposure, ISP, and illumination, can be incorrectly absorbed into Gaussian geometry. Second, unreliable local geometric supervision arises from normal-based constraints near depth discontinuities. To address these issues, we present \textbf{MCSplat}, a multi-view consistent feed-forward Gaussian splatting framework for driving scenes. MCSplat constructs a canonical appearance space from depth-guided multi-camera correspondences to decouple geometry learning from camera-specific appearance. A multi-scale Bilateral Grid Transformer restores raw camera appearance from canonical renderings, enabling photometric supervision without contaminating Gaussian geometry. To improve local geometric reliability, we estimate Fisher-information-based uncertainty from photometric sensitivity and use it to adaptively weight surface-oriented self-supervision. Extensive experiments demonstrate improved reconstruction quality and generalization across driving datasets. Geometry-oriented evaluations…
PDF arXiv
arXiv cs.CVOct 18
Holo4D: Holistic 4D Reconstruction as Geometric Control for Video Diffusion
Yushi Lan, Zeren Jiang, Koichi Namekata, Xingang Pan, Chuanxia Zheng +1
Synthesizing a monocular video from a new camera trajectory requires preserving the observed scene geometry and motion while completing regions that become newly visible. Feed-forward 4D reconstructors recover the structure of the observed scene, whereas video diffusion models provide a generative prior for completing unseen content. We introduce Holo4D, a video reshooting framework that uses reconstructed 3D trajectories as a holistic source-view 4D scaffold. We project these trajectories into the requested views and encode their geometry and source appearance to guide a pretrained video diffusion model. Together with the source video and target cameras, the scaffold grounds generation in reconstructed observations while leaving unsupported regions to the generative prior. Experiments demonstrate the value of trajectory-based conditioning for camera control and novel-view synthesis. The framework accommodates different 4D reconstructors without retraining the video generator, allowing it to benefit from future advances in source reconstruction. More details can be found in our project page.

PDF arXiv

arXiv cs.LGOct 18

Emergent Sparsity: How Many Effective Variables Survive Coarse-Graining in AlphaEarth Embeddings?

Johannes Hirn

AlphaEarth encodes each 10 m land pixel as a 64-component vector intended to support diverse global mapping tasks. Yet many scientific applications work at coarser spatial supports, for which fewer than 64 effective variables may suffice. We ask whether these vectors can be reorganized into ranked global nonlinear coordinates, how many suffice at a chosen reconstruction accuracy, and whether that number decreases with spatial scale. We thus describe effective dimension as a two-control surface over tolerance and spatial scale, rather than one intrinsic number. At each scale, a $β$-VAE scan with a common architecture produces a rank--distortion envelope, giving the smallest retained count achieved within the scanned family at each reconstruction tolerance. At 3% validation tolerance, this number falls from 24 to 7 when the scale is increased from 10 m to 78.9 km, whereas the corresponding PCA rank falls only from 46 to 38. Repeating the analysis on unit-norm targets preserves this trend.

PDF arXiv

arXiv cs.CVOct 18

GeoV2V: Geometry-Grounded Video Diffusion Model for Driving Scene Generation

Yuchen Xi, Yuhan Guo, Chenwei Hou, Zixu Liu, Jin Fang +2

Novel view synthesis under trajectory changes is essential for autonomous driving simulation, yet existing methods struggle to generate consistent videos when extrapolating to unseen viewpoints such as lane shifts. Reconstruction-based approaches maintain geometric consistency but degrade in visual quality under large viewpoint changes, while reconstruct-then-restore methods often produce visually plausible frames yet suffer from geometric and temporal inconsistencies. We present GeoV2V, a geometry-grounded video-to-video diffusion framework that synthesizes temporally coherent driving videos along novel trajectories by integrating LiDAR and depth-derived geometric priors with full-video conditioning in the Wan 2.1 backbone. A key challenge in learning cross-lane transformations is the lack of synchronized multi-trajectory supervision in real-world datasets. To address this, we also introduce Para4D, a synthetic dataset capturing synchronized multi-lane driving videos within dynamic scenes, which provides explicit supervision for lane-shift generation and serves as a strong training prior for geometry-consistent view synthesis. Experiments on Waymo, nuScenes, and Para4D…

PDF arXiv

arXiv cs.CVOct 18

Image Dehazing as In-Context Tabular Inference

Le-Anh Tran

Image dehazing is traditionally addressed through handcrafted physical priors or task-specific deep learning models, often requiring carefully designed architectures or extensive model training. Recent advances in tabular foundation models (TFMs), however, offer an alternative learning paradigm in which continuous relationships can be inferred directly from contextual examples without task-specific model training. Motivated by this capability, this paper investigates whether single-image dehazing can be effectively reformulated as an in-context tabular regression problem and introduces DehazeTab, a framework that estimates atmospheric transmission from structured image-patch representations. Specifically, overlapping patches extracted from hazy images are transformed into tabular samples using handcrafted haze-related descriptors, pretrained visual embeddings, or their hybrid combinations, while corresponding transmission patches are summarized as scalar regression targets. A pretrained TFM then performs in-context regression using labeled reference patches to predict transmission values for unseen images without any further fine-tuning. These predictions are spatially aggregated…

PDF arXiv

arXiv cs.CVOct 18

LC-TSRNet: A Land-Cover-Guided Two-Stage Network for Annual MODIS Surface Reflectance Time Series Reconstruction

Wenjuan Zhang, Xuesong Ma, Zhen Li, Meixiang Wang, Caimeng Yi +3

MODIS surface reflectance (SR) products often suffer from data gaps due to cloud contamination and sensor malfunctions, limiting their applications in long-term and large-scale studies. Most existing methods rely on cloud-free observations from optical time series or introduce SAR time series as auxiliary information. However, due to the coarse spatial resolution of MODIS, cloud-free reference images are difficult to obtain, and SAR imagery is not well matched to the spatial resolution of MODIS, making it difficult to provide effective prior constraints. To address these issues, this study proposes LC-TSRNet for seamless reconstruction of annual MODIS SR products. In the first stage, an annual land cover map is introduced as prior knowledge, and CSPM is designed by leveraging the spectral similarity within identical land cover types, thereby providing prior spectral constraints for missing regions. In the second stage, to address large-area, persistent, and overlapping gaps in MODIS imagery, SMRM is designed to adaptively exploit and integrate multi-scale spatiotemporal contextual information. Meanwhile, to address the limited collaborative utilization of spatio-temporal-spectral…

PDF arXiv

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3D scene

Found by the topic model: 3d scene, gaussian splatting 3dgs, remote sensing.

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