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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 papers43 this week
gaussian splatting 3dgs · 3d gaussian splatting · feed forward 3d212 papers36 this week
small target detection · infrared small target · target detection122 papers26 this week
remote sensing · satellite imagery · earth observation133 papers25 this week
point cloud registration · 3d point clouds · point cloud72 papers18 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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Recent papers

All papers in Research

arXiv cs.CVSep 29
DispFlow-GS: Displacement Flow Supervision with Motion Disentangling for Monocular Deformable 3D Gaussian Splatting
Thai Duy Nguyen, Haitian Zhang, Addison Lin Wang
Accurate dynamic scene reconstruction is important for robotic perception, where temporally consistent representations of dynamic environments are essential. Deformable 3D Gaussian Splatting (3DGS) models dynamic scenes through deformation fields, and recent methods incorporate motion supervision by aligning rendered Gaussian flow with optical flow. However, we find that such Gaussian-flow-based supervision provides only limited improvements in motion modeling. We identify a fundamental limitation of this supervision paradigm, namely a domain gap between rendered Gaussian flow and optical flow. To address this limitation, we propose a motion supervision framework built on Displacement Flow, which splats per-Gaussian 3D displacements onto the image plane to provide direct and stable optimization signals. We further disentangle scene motion from camera motion via intermediate-view rendering, enabling more reliable motion priors and targeted constraints on deformation and geometry. We also observe a discrepancy between motion fidelity and image-based evaluation, where improved motion awareness does not necessarily translate into better rendered image quality or higher image-based…
PDF arXiv
arXiv cs.CVSep 29
SFE-VGGT: Source-Free VGGT Distillation for Event-Based Monocular Depth Estimation
Thai Duy Nguyen, Addison Lin Wang
Recent event-based depth estimation methods successfully transfer geometric priors from vision foundation models via cross-modal distillation. However, their reliance on synchronized RGB-event pairs or depth annotations during training severely restricts practical deployment. To overcome this bottleneck, we propose SFE-VGGT, a novel source-free framework that distills the geometric priors of VGGT to the event domain without any paired RGB observations. Our core idea is to reconstruct surrogate frames directly from the target event stream to act as a frozen geometric teacher, entirely eliminating the need for genuine source RGB data. Crucially, as these surrogate frames inherently yield imperfect and spatially varying supervision, directly distilling from them propagates artifacts. To resolve this, we introduce a novel reliability-aware distillation strategy. This includes Density-Aware Feature Distillation to emphasize informative event regions, and Confidence-Weighted Depth Distillation to dynamically regulate supervision based on relative teacher-student prediction confidence. Meanwhile, we propose a Cross-Frame Relational Consistency loss that enforces temporal geometric…
PDF arXiv
arXiv cs.CVSep 29
Seg3DParts: Segmentation-Grounded Controllable Part-Level 3D Generation
Jiantao Lin, Meixi Chen, Yingjie Xu, Chenbo Fu, Leyi Wu +3
Part-level 3D assets are essential for editing, reassembly, and interaction, yet recovering such structure from a single image remains challenging due to occlusion, ambiguous boundaries, and the need for coherent multi-part reasoning. Existing approaches struggle to achieve both controllable part-level generation and coherent multi-part structure, as part identity and spatial allocation are typically inferred implicitly. We present Seg3DParts, a segmentation-grounded framework for controllable part-level 3D generation from a single image. By treating segmentation as an explicit grounding signal, our method defines part identity during generation, enabling each component to be anchored to a corresponding image region. To ensure coherent assemblies, we introduce structured cross-part interaction that allows components to exchange global context throughout the generative process. As a result, Seg3DParts directly generates well-aligned part meshes in a shared canonical space without post-hoc alignment, supporting flexible and controllable decomposition. We further introduce PartObjectNet, a large-scale dataset with over 200K objects and 1M annotated parts. Experiments demonstrate…
PDF arXiv
arXiv cs.CVSep 29
GlassFormer: Learning Real-time Glass Segmentation using Radar-Depth Fusion
Suhani Grover, Astik Srivastava, Viswas Dinesh, Avinash Sharma, K. Madhava Krishna
Transparent surfaces are ubiquitous in built environments, yet they remain a persistent failure case for robotic perception. RGB cameras perceive the background behind glass rather than the surface itself, while depth sensors such as LiDAR, time-of-flight, and RGB-D often return invalid or background measurements in transparent regions. As a result, systems that rely solely on optical sensing may misinterpret glass walls, doors, or mirrors as free space, compromising safe and reliable navigation. Existing glass segmentation approaches address this by learning visual cues such as reflections, boundaries, and semantic context from RGB images. While effective under favourable lighting and viewing conditions, these cues degrade in low-light environments, under glare, or when glass surfaces are featureless or partially occluded. In this work, we propose a multimodal framework that fuses millimetre-wave radar with RGB-D sensing for real-time transparent surface segmentation. Radar reflects strongly off glass surfaces, providing a geometric cue that remains reliable precisely where vision and depth fail. We exploit this cross-modal inconsistency to generate a radar-guided spatial prior,…
PDF arXiv
arXiv cs.CVSep 29
Does the VGGT Family Need All Its Layers?
Fengyi Zhang, Holger Caesar, Xiangyu Sun, Zheng Zhang, Zi Huang +1
Which layers of a feed-forward geometry model are needed to preserve both camera poses and dense 3D structure? We study layer redundancy in VGGT, $π^3$, and VGGT-$Ω$: 3,018 pruned configurations, scored on seven camera-pose and dense-geometry metrics across four indoor and outdoor datasets. Four findings follow: (i) Removable layers cluster in two redundancy regions: a dominant early region and a narrower late one, while deletions spanning the intervening layers are consistently more disruptive. This recurring pattern holds across models, datasets, and metrics, and contrasts with the middle-to-late redundancy commonly reported in the literature. (ii) Within these regions, we observe that the joint degradation from deleting two intervals is approximately the sum of their individual degradations, reducing the number of model evaluations for pruning search from $O(L^4)$ to $O(L^2)$, where $L$ is the aggregator depth. (iii) We find that CKA provides a cheaper representation-based proxy for interval degradation, offering a practical trade-off between pruning quality and calibration cost. (iv) Closed-form linear calibration recovers accuracy after pruning without end-to-end retraining.…
PDF arXiv
arXiv cs.CVSep 29
AESplat: Advancing Pose-Free Feed-Forward 3D Gaussian Splatting via Decoupled Appearance Modeling
Shiwei Ren, Zhiang Liu, Yongchun Fang, Hongwei Chen
Pose-free feed-forward 3D Gaussian Splatting (3DGS) has demonstrated remarkable potential for generalized novel view synthesis. However, existing methods typically predict Gaussian appearance attributes represented by spherical harmonics (SH) in the same manner, overlooking the fundamental distinction between view-independent and view-dependent appearance, which results in suboptimal rendering quality. In this paper, we present AESplat, a novel and general framework for pose-free feed-forward 3DGS that introduces an effective decoupled appearance modeling strategy based on an analysis of SH, enabling higher-quality rendering. Specifically, AESplat directly derives the zeroth-order SH coefficient, which represents the base view-independent appearance component, from the input images without training. The higher-order SH coefficients are subsequently predicted by a shallow multilayer perceptron equipped with two efficient 3D-aware inductive biases to model view-dependent appearance variations. Extensive experiments across multiple datasets demonstrate that our method significantly outperforms state-of-the-art approaches, achieving a $0.8$ dB improvement in PSNR over the pose-free…
PDF arXiv

Papers

Topics in this field

3d scene · 3d reconstruction · depth estimation268 papers43 this week
gaussian splatting 3dgs · 3d gaussian splatting · feed forward 3d212 papers36 this week
small target detection · infrared small target · target detection122 papers26 this week
remote sensing · satellite imagery · earth observation133 papers25 this week
point cloud registration · 3d point clouds · point cloud72 papers18 this week
camera lidar · 3d object detection · point clouds96 papers14 this week

Recent papers

All papers in Research

arXiv cs.CVSep 29
DispFlow-GS: Displacement Flow Supervision with Motion Disentangling for Monocular Deformable 3D Gaussian Splatting
Thai Duy Nguyen, Haitian Zhang, Addison Lin Wang
Accurate dynamic scene reconstruction is important for robotic perception, where temporally consistent representations of dynamic environments are essential. Deformable 3D Gaussian Splatting (3DGS) models dynamic scenes through deformation fields, and recent methods incorporate motion supervision by aligning rendered Gaussian flow with optical flow. However, we find that such Gaussian-flow-based supervision provides only limited improvements in motion modeling. We identify a fundamental limitation of this supervision paradigm, namely a domain gap between rendered Gaussian flow and optical flow. To address this limitation, we propose a motion supervision framework built on Displacement Flow, which splats per-Gaussian 3D displacements onto the image plane to provide direct and stable optimization signals. We further disentangle scene motion from camera motion via intermediate-view rendering, enabling more reliable motion priors and targeted constraints on deformation and geometry. We also observe a discrepancy between motion fidelity and image-based evaluation, where improved motion awareness does not necessarily translate into better rendered image quality or higher image-based…
PDF arXiv
arXiv cs.CVSep 29
SFE-VGGT: Source-Free VGGT Distillation for Event-Based Monocular Depth Estimation
Thai Duy Nguyen, Addison Lin Wang
Recent event-based depth estimation methods successfully transfer geometric priors from vision foundation models via cross-modal distillation. However, their reliance on synchronized RGB-event pairs or depth annotations during training severely restricts practical deployment. To overcome this bottleneck, we propose SFE-VGGT, a novel source-free framework that distills the geometric priors of VGGT to the event domain without any paired RGB observations. Our core idea is to reconstruct surrogate frames directly from the target event stream to act as a frozen geometric teacher, entirely eliminating the need for genuine source RGB data. Crucially, as these surrogate frames inherently yield imperfect and spatially varying supervision, directly distilling from them propagates artifacts. To resolve this, we introduce a novel reliability-aware distillation strategy. This includes Density-Aware Feature Distillation to emphasize informative event regions, and Confidence-Weighted Depth Distillation to dynamically regulate supervision based on relative teacher-student prediction confidence. Meanwhile, we propose a Cross-Frame Relational Consistency loss that enforces temporal geometric…
PDF arXiv
arXiv cs.CVSep 29
Seg3DParts: Segmentation-Grounded Controllable Part-Level 3D Generation
Jiantao Lin, Meixi Chen, Yingjie Xu, Chenbo Fu, Leyi Wu +3
Part-level 3D assets are essential for editing, reassembly, and interaction, yet recovering such structure from a single image remains challenging due to occlusion, ambiguous boundaries, and the need for coherent multi-part reasoning. Existing approaches struggle to achieve both controllable part-level generation and coherent multi-part structure, as part identity and spatial allocation are typically inferred implicitly. We present Seg3DParts, a segmentation-grounded framework for controllable part-level 3D generation from a single image. By treating segmentation as an explicit grounding signal, our method defines part identity during generation, enabling each component to be anchored to a corresponding image region. To ensure coherent assemblies, we introduce structured cross-part interaction that allows components to exchange global context throughout the generative process. As a result, Seg3DParts directly generates well-aligned part meshes in a shared canonical space without post-hoc alignment, supporting flexible and controllable decomposition. We further introduce PartObjectNet, a large-scale dataset with over 200K objects and 1M annotated parts. Experiments demonstrate…
PDF arXiv
arXiv cs.CVSep 29
GlassFormer: Learning Real-time Glass Segmentation using Radar-Depth Fusion
Suhani Grover, Astik Srivastava, Viswas Dinesh, Avinash Sharma, K. Madhava Krishna
Transparent surfaces are ubiquitous in built environments, yet they remain a persistent failure case for robotic perception. RGB cameras perceive the background behind glass rather than the surface itself, while depth sensors such as LiDAR, time-of-flight, and RGB-D often return invalid or background measurements in transparent regions. As a result, systems that rely solely on optical sensing may misinterpret glass walls, doors, or mirrors as free space, compromising safe and reliable navigation. Existing glass segmentation approaches address this by learning visual cues such as reflections, boundaries, and semantic context from RGB images. While effective under favourable lighting and viewing conditions, these cues degrade in low-light environments, under glare, or when glass surfaces are featureless or partially occluded. In this work, we propose a multimodal framework that fuses millimetre-wave radar with RGB-D sensing for real-time transparent surface segmentation. Radar reflects strongly off glass surfaces, providing a geometric cue that remains reliable precisely where vision and depth fail. We exploit this cross-modal inconsistency to generate a radar-guided spatial prior,…
PDF arXiv
arXiv cs.CVSep 29
Does the VGGT Family Need All Its Layers?
Fengyi Zhang, Holger Caesar, Xiangyu Sun, Zheng Zhang, Zi Huang +1
Which layers of a feed-forward geometry model are needed to preserve both camera poses and dense 3D structure? We study layer redundancy in VGGT, $π^3$, and VGGT-$Ω$: 3,018 pruned configurations, scored on seven camera-pose and dense-geometry metrics across four indoor and outdoor datasets. Four findings follow: (i) Removable layers cluster in two redundancy regions: a dominant early region and a narrower late one, while deletions spanning the intervening layers are consistently more disruptive. This recurring pattern holds across models, datasets, and metrics, and contrasts with the middle-to-late redundancy commonly reported in the literature. (ii) Within these regions, we observe that the joint degradation from deleting two intervals is approximately the sum of their individual degradations, reducing the number of model evaluations for pruning search from $O(L^4)$ to $O(L^2)$, where $L$ is the aggregator depth. (iii) We find that CKA provides a cheaper representation-based proxy for interval degradation, offering a practical trade-off between pruning quality and calibration cost. (iv) Closed-form linear calibration recovers accuracy after pruning without end-to-end retraining.…
PDF arXiv
arXiv cs.CVSep 29
AESplat: Advancing Pose-Free Feed-Forward 3D Gaussian Splatting via Decoupled Appearance Modeling
Shiwei Ren, Zhiang Liu, Yongchun Fang, Hongwei Chen
Pose-free feed-forward 3D Gaussian Splatting (3DGS) has demonstrated remarkable potential for generalized novel view synthesis. However, existing methods typically predict Gaussian appearance attributes represented by spherical harmonics (SH) in the same manner, overlooking the fundamental distinction between view-independent and view-dependent appearance, which results in suboptimal rendering quality. In this paper, we present AESplat, a novel and general framework for pose-free feed-forward 3DGS that introduces an effective decoupled appearance modeling strategy based on an analysis of SH, enabling higher-quality rendering. Specifically, AESplat directly derives the zeroth-order SH coefficient, which represents the base view-independent appearance component, from the input images without training. The higher-order SH coefficients are subsequently predicted by a shallow multilayer perceptron equipped with two efficient 3D-aware inductive biases to model view-dependent appearance variations. Extensive experiments across multiple datasets demonstrate that our method significantly outperforms state-of-the-art approaches, achieving a $0.8$ dB improvement in PSNR over the pose-free…
PDF arXiv

Active contributors · 90 days

Activity · 12 weeks

Fields · AI · updated 34m ago

3D scene

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

162papers this week−12.0% vs last week

All papers · 903 With code · 28

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804 papers this month

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.