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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 realism for interactive or dynamic use.
- 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 greatly from training inputs.
- 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
Dense correspondence matching without spatio-temporal priors1 paper raises it · 1 addresses it
Existing correspondence methods fail when transformations break physical continuity, limiting applications like image editing.
- Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matchingraises · addresses
Physics and geometry coherence in 3D scene generation1 paper raises it · 1 addresses it
Generated 3D scenes often lack fine-grained spatial and physical plausibility between interacting objects.
- Tetris3D: 3D Scene Generation With Objects That Fit Togetherraises · addresses
Metric distance estimation in 3D reconstruction1 paper raises it · 1 addresses it
Current benchmarks do not directly evaluate point-to-point distance accuracy, a fundamental requirement for many 3D tasks.
- 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 estimation268 papers45 this week
gaussian splatting 3dgs · 3d gaussian splatting · feed forward 3d212 papers38 this week
remote sensing · satellite imagery · earth observation133 papers26 this week
small target detection · infrared small target · target detection122 papers26 this week
point cloud registration · 3d point clouds · point cloud72 papers19 this week
camera lidar · 3d object detection · point clouds96 papers14 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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All papers in Research

arXiv cs.CVOct 1
Generative Cinematographer: Composing Camera and Object Motion in 3D
Jiahan Zhang, Chaohao Yang, Namitha Guruprasad, Vivekjyoti Banerjee, Trong-Tung Nguyen +2
Current controllable video generation systems often rely on 2D motion trajectories or sparse drag signals for object motion. These controls are ambiguous because the same 2D trajectory can correspond to different 3D motions, especially when the camera and objects move simultaneously. We present Generative Cinematographer (GenCine), a system that lifts a single image into an editable 3D scene scaffold where artists jointly author camera and foreground motion. Artists specify a camera path and move selected foreground regions using local 3D motion handles. Several handles can move different parts of a subject independently, providing a piecewise-rigid approximation to non-rigid motion without a physics simulator or category-specific prior. To communicate these controls to a pretrained video model, we project them into guidance maps. These maps record where the controlled regions appear in each frame, assign each handle a fixed color across frames and encode the current 3D positions of its controlled points in the same world coordinate system as the background. This lets us describe object motion relative to the scene even as the camera moves. For training, we recover controls from…
PDF arXiv
arXiv cs.CVOct 1
4Director: Controlling Video World Models with Rigid 3D Geometry
Wei Cao, Hao Zhang, Vikram Voleti, Yuqun Wu, Mallikarjun B R +3
Precise control over camera and object motion is essential for professional video production. Existing methods control objects only coarsely, through image-plane cues that are ambiguous in depth and rotation or through 3D tracks and blobs that lack complete geometry and lose consistency across viewpoint changes. We introduce 4Director, a video world model conditioned on an explicit 4D scene representation: each object is reconstructed once from the input image as a canonical mesh and moved by one prescribed rigid transformation per frame. This representation provides an intuitive 3D control interface and prevents unobserved geometry from being regenerated independently in every frame. We render the controlled scene as a depth video and introduce a Motion Adapter that transforms this geometric scaffold into video while synthesizing view-consistent appearance, illumination, and non-rigid dynamics. For training, we construct RealCOD-Rigid, a new dataset of 20,774 clips annotated with rigid 3D scenes by our automatic pipeline. We further introduce Identity-Gated IoU (IG-IoU), which jointly evaluates adherence to prescribed object motion and preservation of object identity.…
PDF arXiv
arXiv cs.CVOct 1
Weather-Aware Domain Adaptation for Street-View Weather Recognition
Hossein Maghsoumi, George Atia, Yaser P. Fallah
Adverse conditions such as rain, snow, fog, and dust remain challenging for camera-based perception in autonomous driving. We study multi-class weather recognition from street-view images under domain shift, where most available training data come from non-street-view sources that differ markedly from real driving scenes. We propose Weather-Aware Adversarial Discriminative Domain Adaptation (WA-ADDA), which conditions the domain discriminator on predicted weather to promote features that are both domain-invariant and weather-sensitive. We also assemble a multi-dataset benchmark by unifying diverse non-street-view weather collections as sources and real street-view images as targets, and define a standardized evaluation protocol with macro accuracy as the primary metric. Across backbones (ResNet-50, EfficientNet, VGG, DenseNet), WA-ADDA consistently improves street-view performance and yields strong per-class recalls in challenging conditions while preserving clear-weather accuracy. These findings highlight the feasibility of domain-adapted weather recognition and the value of our benchmark for advancing robust, on-board perception.
PDF arXiv
arXiv cs.CVOct 1
Comparing a gradient boosting algorithm to the GOES FDC for wildfire detection
Asaf Vanunu, Boaz Nadler, Arnon Karnieli
Wildfires pose severe risks to human life, ecosystems, and property. This study presents a machine learning approach for wildfire detection from GOES ABI imagery. A CatBoost model was trained on a large dataset with thousands of ABI images and over 300,000 matching VIIRS fire detections. An evaluation on a separate dataset across five regions showed that the learned CatBoost model outperformed the operational GOES Fire Detection and Characterization (FDC) product. It achieved higher precision, recall, and F1 scores both within and outside the training area. The CatBoost model achieved F1 scores that were 0.16 to 0.38 higher than the GOES FDC in all regions. In addition, out of 51 historical fire events, the CatBoost detected 26 fires before both VIIRS and GOES FDC, compared to only six earlier detections by the GOES FDC. Importantly, the CatBoost model achieved accurate wildfire detection also during nighttime, whereas the GOES FDC obtained very low recall values, around 0.03. This study demonstrates that machine learning models may offer significant improvements over existing geostationary fire products, including higher accuracy, fewer false alarms, and earlier detection.
PDF arXiv
arXiv cs.CVOct 1
CLoSeR: Closing the Loop for Long-Context Streaming Reconstruction
Moyang Li, Zihan Zhu, Wei Zhang, Marc Pollefeys, Daniel Barath
Feedforward foundation models have recently shown remarkable 3D reconstruction capabilities. However, existing models exhibit large tracking drift in long-context streaming reconstruction due to error accumulation. In this paper, we revisit loop closure with streaming reconstruction foundation models to enable accurate, drift-free, kilometer-scale reconstruction. Specifically, our method detects loop candidates through global descriptor retrieval, and constructs loop-conditioned windows to estimate the relative poses between looped frames. Given the observation that our adopted streaming reconstruction backbone produces a globally consistent scale, we optimize all frame poses on the SE(3) manifold with sequential and loop closure constraints, avoiding the pose graph optimization on the Sim(3) or higher-dimensional SL(4) manifolds employed in prior works. Extensive experiments show that our method reduces drift and produces consistent geometry on kilometer-scale sequences, significantly outperforming the state of the art. Code is available at https://github.com/MoyangLi00/CLoSeR.git.
PDF arXiv
arXiv cs.CVOct 1
DecomVoxel: Harnessing 3D-Native Priors with Guided In-situ Denoising Optimization for Decompositional Scene Reconstruction
Junfeng Ni, Zirui Zhou, Yixin Chen, Yu Liu, Nan Jiang +3
Decompositional scene reconstruction aims to reconstruct high-quality objects and background, yet existing methods still struggle with the level of quality under heavy occlusions. While generative priors offer a potential solution, 2D image-based priors often suffer from multi-view inconsistency due to a lack of 3D awareness. Conversely, 3D-native priors provide stronger structural inductive biases but frequently lead to spatial drift and misalignment within complex scenes. To address these issues, we propose DecomVoxel, formulating object completion as a guided in-situ denoising optimization that bridges 3D-native priors with neural scene reconstruction. Our framework introduces a reformulated epsilon-based distillation loss to ensure stable latent refinement, alongside adaptive spatial guidance that utilizes occupied and vacant anchors with temporal annealing to suppress generative hallucinations and mitigate spatial drift. Experiments on Replica and ScanNet++ show that DecomVoxel significantly outperforms state-of-the-art methods while faithfully preserving the original spatial layout, structural fidelity, and style-consistent texture. Our method pushes the boundary of…
PDF arXiv

Papers

Topics in this field

3d scene · 3d reconstruction · depth estimation268 papers45 this week
gaussian splatting 3dgs · 3d gaussian splatting · feed forward 3d212 papers38 this week
remote sensing · satellite imagery · earth observation133 papers26 this week
small target detection · infrared small target · target detection122 papers26 this week
point cloud registration · 3d point clouds · point cloud72 papers19 this week
camera lidar · 3d object detection · point clouds96 papers14 this week

Active contributors · 90 days

Activity · 12 weeks

Fields · AI · updated 56m ago

3D scene

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

168papers this week−9.2% vs last week

All papers · 903 With code · 27

Newest Most relevant

809 papers this month

Recent papers

All papers in Research

arXiv cs.CVOct 1
Generative Cinematographer: Composing Camera and Object Motion in 3D
Jiahan Zhang, Chaohao Yang, Namitha Guruprasad, Vivekjyoti Banerjee, Trong-Tung Nguyen +2
Current controllable video generation systems often rely on 2D motion trajectories or sparse drag signals for object motion. These controls are ambiguous because the same 2D trajectory can correspond to different 3D motions, especially when the camera and objects move simultaneously. We present Generative Cinematographer (GenCine), a system that lifts a single image into an editable 3D scene scaffold where artists jointly author camera and foreground motion. Artists specify a camera path and move selected foreground regions using local 3D motion handles. Several handles can move different parts of a subject independently, providing a piecewise-rigid approximation to non-rigid motion without a physics simulator or category-specific prior. To communicate these controls to a pretrained video model, we project them into guidance maps. These maps record where the controlled regions appear in each frame, assign each handle a fixed color across frames and encode the current 3D positions of its controlled points in the same world coordinate system as the background. This lets us describe object motion relative to the scene even as the camera moves. For training, we recover controls from…
PDF arXiv
arXiv cs.CVOct 1
4Director: Controlling Video World Models with Rigid 3D Geometry
Wei Cao, Hao Zhang, Vikram Voleti, Yuqun Wu, Mallikarjun B R +3
Precise control over camera and object motion is essential for professional video production. Existing methods control objects only coarsely, through image-plane cues that are ambiguous in depth and rotation or through 3D tracks and blobs that lack complete geometry and lose consistency across viewpoint changes. We introduce 4Director, a video world model conditioned on an explicit 4D scene representation: each object is reconstructed once from the input image as a canonical mesh and moved by one prescribed rigid transformation per frame. This representation provides an intuitive 3D control interface and prevents unobserved geometry from being regenerated independently in every frame. We render the controlled scene as a depth video and introduce a Motion Adapter that transforms this geometric scaffold into video while synthesizing view-consistent appearance, illumination, and non-rigid dynamics. For training, we construct RealCOD-Rigid, a new dataset of 20,774 clips annotated with rigid 3D scenes by our automatic pipeline. We further introduce Identity-Gated IoU (IG-IoU), which jointly evaluates adherence to prescribed object motion and preservation of object identity.…
PDF arXiv
arXiv cs.CVOct 1
Weather-Aware Domain Adaptation for Street-View Weather Recognition
Hossein Maghsoumi, George Atia, Yaser P. Fallah
Adverse conditions such as rain, snow, fog, and dust remain challenging for camera-based perception in autonomous driving. We study multi-class weather recognition from street-view images under domain shift, where most available training data come from non-street-view sources that differ markedly from real driving scenes. We propose Weather-Aware Adversarial Discriminative Domain Adaptation (WA-ADDA), which conditions the domain discriminator on predicted weather to promote features that are both domain-invariant and weather-sensitive. We also assemble a multi-dataset benchmark by unifying diverse non-street-view weather collections as sources and real street-view images as targets, and define a standardized evaluation protocol with macro accuracy as the primary metric. Across backbones (ResNet-50, EfficientNet, VGG, DenseNet), WA-ADDA consistently improves street-view performance and yields strong per-class recalls in challenging conditions while preserving clear-weather accuracy. These findings highlight the feasibility of domain-adapted weather recognition and the value of our benchmark for advancing robust, on-board perception.
PDF arXiv
arXiv cs.CVOct 1
Comparing a gradient boosting algorithm to the GOES FDC for wildfire detection
Asaf Vanunu, Boaz Nadler, Arnon Karnieli
Wildfires pose severe risks to human life, ecosystems, and property. This study presents a machine learning approach for wildfire detection from GOES ABI imagery. A CatBoost model was trained on a large dataset with thousands of ABI images and over 300,000 matching VIIRS fire detections. An evaluation on a separate dataset across five regions showed that the learned CatBoost model outperformed the operational GOES Fire Detection and Characterization (FDC) product. It achieved higher precision, recall, and F1 scores both within and outside the training area. The CatBoost model achieved F1 scores that were 0.16 to 0.38 higher than the GOES FDC in all regions. In addition, out of 51 historical fire events, the CatBoost detected 26 fires before both VIIRS and GOES FDC, compared to only six earlier detections by the GOES FDC. Importantly, the CatBoost model achieved accurate wildfire detection also during nighttime, whereas the GOES FDC obtained very low recall values, around 0.03. This study demonstrates that machine learning models may offer significant improvements over existing geostationary fire products, including higher accuracy, fewer false alarms, and earlier detection.
PDF arXiv
arXiv cs.CVOct 1
CLoSeR: Closing the Loop for Long-Context Streaming Reconstruction
Moyang Li, Zihan Zhu, Wei Zhang, Marc Pollefeys, Daniel Barath
Feedforward foundation models have recently shown remarkable 3D reconstruction capabilities. However, existing models exhibit large tracking drift in long-context streaming reconstruction due to error accumulation. In this paper, we revisit loop closure with streaming reconstruction foundation models to enable accurate, drift-free, kilometer-scale reconstruction. Specifically, our method detects loop candidates through global descriptor retrieval, and constructs loop-conditioned windows to estimate the relative poses between looped frames. Given the observation that our adopted streaming reconstruction backbone produces a globally consistent scale, we optimize all frame poses on the SE(3) manifold with sequential and loop closure constraints, avoiding the pose graph optimization on the Sim(3) or higher-dimensional SL(4) manifolds employed in prior works. Extensive experiments show that our method reduces drift and produces consistent geometry on kilometer-scale sequences, significantly outperforming the state of the art. Code is available at https://github.com/MoyangLi00/CLoSeR.git.
PDF arXiv
arXiv cs.CVOct 1
DecomVoxel: Harnessing 3D-Native Priors with Guided In-situ Denoising Optimization for Decompositional Scene Reconstruction
Junfeng Ni, Zirui Zhou, Yixin Chen, Yu Liu, Nan Jiang +3
Decompositional scene reconstruction aims to reconstruct high-quality objects and background, yet existing methods still struggle with the level of quality under heavy occlusions. While generative priors offer a potential solution, 2D image-based priors often suffer from multi-view inconsistency due to a lack of 3D awareness. Conversely, 3D-native priors provide stronger structural inductive biases but frequently lead to spatial drift and misalignment within complex scenes. To address these issues, we propose DecomVoxel, formulating object completion as a guided in-situ denoising optimization that bridges 3D-native priors with neural scene reconstruction. Our framework introduces a reformulated epsilon-based distillation loss to ensure stable latent refinement, alongside adaptive spatial guidance that utilizes occupied and vacant anchors with temporal annealing to suppress generative hallucinations and mitigate spatial drift. Experiments on Replica and ScanNet++ show that DecomVoxel significantly outperforms state-of-the-art methods while faithfully preserving the original spatial layout, structural fidelity, and style-consistent texture. Our method pushes the boundary of…
PDF arXiv

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 realism for interactive or dynamic use.
- 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 greatly from training inputs.
- 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
Dense correspondence matching without spatio-temporal priors1 paper raises it · 1 addresses it
Existing correspondence methods fail when transformations break physical continuity, limiting applications like image editing.
- Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matchingraises · addresses
Physics and geometry coherence in 3D scene generation1 paper raises it · 1 addresses it
Generated 3D scenes often lack fine-grained spatial and physical plausibility between interacting objects.
- Tetris3D: 3D Scene Generation With Objects That Fit Togetherraises · addresses
Metric distance estimation in 3D reconstruction1 paper raises it · 1 addresses it
Current benchmarks do not directly evaluate point-to-point distance accuracy, a fundamental requirement for many 3D tasks.
- Pumpire: Unified Benchmark for Metric Distance Estimationraises · addresses

Named by an AI from these papers' abstracts.