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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 scenes when camera poses differ significantly from training views.
- 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 scene generation1 paper raises it · 1 addresses it
Generated 3D scenes often lack physically plausible object interactions and spatial compatibility.
- Tetris3D: 3D Scene Generation With Objects That Fit Togetherraises · addresses
Dense correspondence matching beyond rigid motion1 paper raises it · 1 addresses it
Existing correspondence methods fail 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 geometry.
- Pumpire: Unified Benchmark for Metric Distance Estimationraises · addresses

Named by an AI from these papers' abstracts.

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Topics in this field

3d scene · 3d reconstruction · depth estimation262 papers45 this week
gaussian splatting 3dgs · 3d gaussian splatting · feed forward 3d209 papers41 this week
remote sensing · satellite imagery · earth observation127 papers25 this week
small target detection · infrared small target · target detection114 papers22 this week
point cloud registration · 3d point clouds · point cloud69 papers21 this week
camera lidar · 3d object detection · point clouds95 papers16 this week

Topics in this field

Active contributors · 90 days

Active contributors

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arXiv cs.CVOct 17
O-SCD++: Replay-Free Online Scene Change Detection via Recursive Evidence Fusion
Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Timothy Patten, Donald G. Dansereau +2
Online scene change detection requires an agent to detect what has changed in a previously mapped environment while it is still on site, observing from unconstrained viewpoints and without access to future observations. This causal setting is strictly harder than its offline counterpart, and online methods have consistently trailed offline accuracy. O-SCD closed that gap by treating change as a persistent property of a 3D Gaussian splatting map of the reference scene, surpassing the strongest offline methods in change detection accuracy while operating at over 10 frames per second. O-SCD optimizes a change field over the Gaussians of the reference map, refitting it to all past cue maps at every frame, a step that consumes more than half of the per-frame budget and forces accuracy to be traded against speed. We present a replay-free formulation that eliminates this trade-off. Because the reference map stays fixed, rendering the change field is a linear operation. Each new observation can therefore be carried onto the map's Gaussians directly, by a single backward pass of the renderer, which reduces multi-view fusion to an exact two-scalar recursion per Gaussian. Past observations…
PDF arXiv
arXiv cs.CVOct 17
Spatons: Spatiotonally Localized Building Blocks for Efficient XR Predistortion
Mohammadjavad Maheronnaghsh, Sushant Kondguli, Steve Mann, Andreas Moshovos
We present Spatons, a family of compositional, spatially localized neural operators for computationally efficient XR (extended reality) predistortion and distortion compensation. XR displays (AR, VR, and MR) suffer from spatial (e.g., geometric) and tonal (e.g., colour) distortions. Frames can be pre-distorted so that the projected image looks as intended. Distortions vary with manufacturing tolerances and projection systems, motivating programmable compensation. Untethered, battery-operated XR systems also require low energy and latency. A Spaton is a hybrid patch-to-pixel block combining tiny Multi-Layer Perceptrons (MLPs) with fixed-function interpolation. Using Spatons, we build programmable, computationally efficient pipelines for varied distortion scenarios. On the full spatiotonal Scenario 5, Sequential-5' matches CompenNet++ in SSIM (0.944 vs. 0.944) and closely matches its PSNR (27.5 vs. 27.7 dB), with substantially lower complexity. We demonstrate a flexible Spaton solution with 6 times fewer FLOPs and 106 times fewer parameters than CompenNet++, and 4-10 times fewer memory transfers than UNet++. We characterize software implementations and analytical hardware…
PDF arXiv
arXiv cs.CVOct 17
Hexagonal Basis Projectors for Model-Based Iterative CT Reconstruction
Ke Chen, Alireza Entezari
Image representation affects both the forward model and quantitative feature recovery in model-based iterative CT reconstruction. For isotropically bandlimited signals, the hexagonal lattice is the most sampling-efficient lattice in two dimensions, with greater directional isotropy and a larger alias-free bandwidth than the Cartesian grid at equal density. We develop constant and linear hexagonal projectors for two-dimensional fan-beam CT using box spline calculus. Both bases form a partition of unity on common coefficient centers; the linear tent also reproduces affine functions. We derive analytic ray footprints and effective detector-blur expressions and implement efficient GPU forward and backprojection. On simulated FORBILD data, the linear model improves on Cartesian and hexagonal pixels, with its best noisy-data accuracy at the coarser resolution. Real-world 2DeteCT comparisons use high-dose, high-resolution Cartesian reconstructions as pseudo ground truth and show competitive image and feature agreement. These results establish a practical hexagonal representation for quantitative CT.
PDF arXiv
arXiv cs.CVOct 16
Landmark-Free and Scale-Blind Multi-View Registration of Preoperative Liver Models to Intraoperative Surfaces
Jiaming Feng, Gerardo Loza Galindo, Shahid Farid, Sharib Ali
Augmented-reality guidance in laparoscopic liver resection requires aligning preoperative 3D liver models from CT/MRI with intraoperative surfaces. Monocular reconstruction in most cases cannot guarantee scale, albeit rigid-motion registration requires scale as an input. Our proposed framework ``SimCompose'' estimates the full similarity transformation $\Sim$, including scale, without relying on landmarks and applies the same transformation across multiple views with known relative geometry. However, reliable detection of registration failures remains challenging in surgical settings, where ground-truth alignment is generally unavailable. For this, we propose a geometry-based selector that evaluates candidate transformations without requiring ground truth. Large disagreement among these transformations is used to flag potentially unreliable registrations. Finally, we propose to use a non-rigid refinement based on a low-dimensional deformation model from Neo-Hookean simulations in the Simulation Open Framework Architecture (SOFA), without additional network training. SimCompose is trained exclusively on synthetic partial views. Its encoder processes point clouds normalised to a…
PDF arXiv
arXiv cs.CVOct 16
Precision Cotton Disease Management: Leveraging UAV Imagery and Custom YOLO Deep Learning Framework for Enhanced Detection
1. Anwar Iqbal*1 aiqbal. rime23smme@student. nust. edu. pk 2. Kunwar Faraz Ahmad Khan1 kunwar. faraz@smme. nust. edu. pk 3. Shahbaz Khan1 shahbaz. khan@smme. nust. edu. pk 4. Khawaja Fahad Iqbal1 fahad. iqbal@smme. nust. edu. pk 5. Sara Ali1, 2 sara. baber@smme. nust. edu. pk 6. Saqib Nazir1 saqib. nazir@smme. nust. edu. pk 7. Muhammad Azhar Javed3 muhammadazhar6231@gmail. com 1 Department of Robotics, Artificial Intelligence, SMME, National University of Science +9
Cotton sustains the textile economies of over 80 countries with an annual yield of 113 million bales, yet 30-60% of yield losses are still attributed to viral, bacterial, and fungal diseases that remain undetected until manual field scouting. To address this critical issue, the integration of Unmanned Aerial Vehicle (UAV) imagery with deep learning offers a scalable early-warning system; however, existing models suffer from low early-stage accuracy, poor transferability, and high computational cost. In this paper, we propose Cotton-Vision, a fully convolutional single-stage detector that synergizes three lightweight architectural innovations: Ghost Bottleneck modules for a 20% parameter reduction, Efficient Channel Attention (ECA) for adaptive feature recalibration, and Cross-Stage Partial (CSP) fusion for enriched gradient flow. The network is trained on a novel dataset collected across seven cotton fields in the Punjab province of Pakistan, comprising over 2,700 high-resolution drone images. Extensive data augmentation (rotation, shear, exposure, and saturation) expands the dataset to 11,704 training samples. On a held-out test set, Cotton-Vision achieves a precision of 97.7%,…
PDF arXiv
arXiv cs.CVOct 16
SERO: Semantic Prediction for Single-Step Object Removal
Amreen Hasheem Siraj, Alireza Javanmardi, Pragati Jaiswal, Didier Stricker, Alain Pagani
Object removal and semantic inpainting of missing regions remain inherently ill-posed, as multiple plausible completions may exist for a given scene. Recent diffusion-based methods have demonstrated strong potential for high-quality object removal; however, they still face two key limitations. First, although they can synthesise sharp and high-resolution images, they often struggle to complete removed regions in a semantically consistent and contextually meaningful manner, where the generated content should agree with the surrounding scene layout and object relationships rather than merely appear visually realistic. Second, their iterative denoising process makes them computationally expensive, limiting their practicality for downstream applications. We propose SERO, a semantic prediction framework for single-step object removal. Our model predicts a hidden-region semantic prior in embedding space, which guides a latent bridge-matching generator toward plausible scene completion by providing both where to edit and what structure is likely to exist behind the removed object. This enables visually realistic and contextually coherent completions in one forward pass. Experiments on…
PDF arXiv

Papers

Topics in this field

3d scene · 3d reconstruction · depth estimation262 papers45 this week
gaussian splatting 3dgs · 3d gaussian splatting · feed forward 3d209 papers41 this week
remote sensing · satellite imagery · earth observation127 papers25 this week
small target detection · infrared small target · target detection114 papers22 this week
point cloud registration · 3d point clouds · point cloud69 papers21 this week
camera lidar · 3d object detection · point clouds95 papers16 this week

Active contributors · 90 days

Recent papers

All in Research →

arXiv cs.CVOct 17
O-SCD++: Replay-Free Online Scene Change Detection via Recursive Evidence Fusion
Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Timothy Patten, Donald G. Dansereau +2
Online scene change detection requires an agent to detect what has changed in a previously mapped environment while it is still on site, observing from unconstrained viewpoints and without access to future observations. This causal setting is strictly harder than its offline counterpart, and online methods have consistently trailed offline accuracy. O-SCD closed that gap by treating change as a persistent property of a 3D Gaussian splatting map of the reference scene, surpassing the strongest offline methods in change detection accuracy while operating at over 10 frames per second. O-SCD optimizes a change field over the Gaussians of the reference map, refitting it to all past cue maps at every frame, a step that consumes more than half of the per-frame budget and forces accuracy to be traded against speed. We present a replay-free formulation that eliminates this trade-off. Because the reference map stays fixed, rendering the change field is a linear operation. Each new observation can therefore be carried onto the map's Gaussians directly, by a single backward pass of the renderer, which reduces multi-view fusion to an exact two-scalar recursion per Gaussian. Past observations…
PDF arXiv
arXiv cs.CVOct 17
Spatons: Spatiotonally Localized Building Blocks for Efficient XR Predistortion
Mohammadjavad Maheronnaghsh, Sushant Kondguli, Steve Mann, Andreas Moshovos
We present Spatons, a family of compositional, spatially localized neural operators for computationally efficient XR (extended reality) predistortion and distortion compensation. XR displays (AR, VR, and MR) suffer from spatial (e.g., geometric) and tonal (e.g., colour) distortions. Frames can be pre-distorted so that the projected image looks as intended. Distortions vary with manufacturing tolerances and projection systems, motivating programmable compensation. Untethered, battery-operated XR systems also require low energy and latency. A Spaton is a hybrid patch-to-pixel block combining tiny Multi-Layer Perceptrons (MLPs) with fixed-function interpolation. Using Spatons, we build programmable, computationally efficient pipelines for varied distortion scenarios. On the full spatiotonal Scenario 5, Sequential-5' matches CompenNet++ in SSIM (0.944 vs. 0.944) and closely matches its PSNR (27.5 vs. 27.7 dB), with substantially lower complexity. We demonstrate a flexible Spaton solution with 6 times fewer FLOPs and 106 times fewer parameters than CompenNet++, and 4-10 times fewer memory transfers than UNet++. We characterize software implementations and analytical hardware…
PDF arXiv
arXiv cs.CVOct 17
Hexagonal Basis Projectors for Model-Based Iterative CT Reconstruction
Ke Chen, Alireza Entezari
Image representation affects both the forward model and quantitative feature recovery in model-based iterative CT reconstruction. For isotropically bandlimited signals, the hexagonal lattice is the most sampling-efficient lattice in two dimensions, with greater directional isotropy and a larger alias-free bandwidth than the Cartesian grid at equal density. We develop constant and linear hexagonal projectors for two-dimensional fan-beam CT using box spline calculus. Both bases form a partition of unity on common coefficient centers; the linear tent also reproduces affine functions. We derive analytic ray footprints and effective detector-blur expressions and implement efficient GPU forward and backprojection. On simulated FORBILD data, the linear model improves on Cartesian and hexagonal pixels, with its best noisy-data accuracy at the coarser resolution. Real-world 2DeteCT comparisons use high-dose, high-resolution Cartesian reconstructions as pseudo ground truth and show competitive image and feature agreement. These results establish a practical hexagonal representation for quantitative CT.
PDF arXiv
arXiv cs.CVOct 16
Landmark-Free and Scale-Blind Multi-View Registration of Preoperative Liver Models to Intraoperative Surfaces
Jiaming Feng, Gerardo Loza Galindo, Shahid Farid, Sharib Ali
Augmented-reality guidance in laparoscopic liver resection requires aligning preoperative 3D liver models from CT/MRI with intraoperative surfaces. Monocular reconstruction in most cases cannot guarantee scale, albeit rigid-motion registration requires scale as an input. Our proposed framework ``SimCompose'' estimates the full similarity transformation $\Sim$, including scale, without relying on landmarks and applies the same transformation across multiple views with known relative geometry. However, reliable detection of registration failures remains challenging in surgical settings, where ground-truth alignment is generally unavailable. For this, we propose a geometry-based selector that evaluates candidate transformations without requiring ground truth. Large disagreement among these transformations is used to flag potentially unreliable registrations. Finally, we propose to use a non-rigid refinement based on a low-dimensional deformation model from Neo-Hookean simulations in the Simulation Open Framework Architecture (SOFA), without additional network training. SimCompose is trained exclusively on synthetic partial views. Its encoder processes point clouds normalised to a…
PDF arXiv
arXiv cs.CVOct 16
Precision Cotton Disease Management: Leveraging UAV Imagery and Custom YOLO Deep Learning Framework for Enhanced Detection
1. Anwar Iqbal*1 aiqbal. rime23smme@student. nust. edu. pk 2. Kunwar Faraz Ahmad Khan1 kunwar. faraz@smme. nust. edu. pk 3. Shahbaz Khan1 shahbaz. khan@smme. nust. edu. pk 4. Khawaja Fahad Iqbal1 fahad. iqbal@smme. nust. edu. pk 5. Sara Ali1, 2 sara. baber@smme. nust. edu. pk 6. Saqib Nazir1 saqib. nazir@smme. nust. edu. pk 7. Muhammad Azhar Javed3 muhammadazhar6231@gmail. com 1 Department of Robotics, Artificial Intelligence, SMME, National University of Science +9
Cotton sustains the textile economies of over 80 countries with an annual yield of 113 million bales, yet 30-60% of yield losses are still attributed to viral, bacterial, and fungal diseases that remain undetected until manual field scouting. To address this critical issue, the integration of Unmanned Aerial Vehicle (UAV) imagery with deep learning offers a scalable early-warning system; however, existing models suffer from low early-stage accuracy, poor transferability, and high computational cost. In this paper, we propose Cotton-Vision, a fully convolutional single-stage detector that synergizes three lightweight architectural innovations: Ghost Bottleneck modules for a 20% parameter reduction, Efficient Channel Attention (ECA) for adaptive feature recalibration, and Cross-Stage Partial (CSP) fusion for enriched gradient flow. The network is trained on a novel dataset collected across seven cotton fields in the Punjab province of Pakistan, comprising over 2,700 high-resolution drone images. Extensive data augmentation (rotation, shear, exposure, and saturation) expands the dataset to 11,704 training samples. On a held-out test set, Cotton-Vision achieves a precision of 97.7%,…
PDF arXiv
arXiv cs.CVOct 16
SERO: Semantic Prediction for Single-Step Object Removal
Amreen Hasheem Siraj, Alireza Javanmardi, Pragati Jaiswal, Didier Stricker, Alain Pagani
Object removal and semantic inpainting of missing regions remain inherently ill-posed, as multiple plausible completions may exist for a given scene. Recent diffusion-based methods have demonstrated strong potential for high-quality object removal; however, they still face two key limitations. First, although they can synthesise sharp and high-resolution images, they often struggle to complete removed regions in a semantically consistent and contextually meaningful manner, where the generated content should agree with the surrounding scene layout and object relationships rather than merely appear visually realistic. Second, their iterative denoising process makes them computationally expensive, limiting their practicality for downstream applications. We propose SERO, a semantic prediction framework for single-step object removal. Our model predicts a hidden-region semantic prior in embedding space, which guides a latent bridge-matching generator toward plausible scene completion by providing both where to edit and what structure is likely to exist behind the removed object. This enables visually realistic and contextually coherent completions in one forward pass. Experiments on…
PDF arXiv

Activity · 12 weeks

Coming with more sources: repos

Fields · AI · updated 1h ago

3D scene

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

170papers this week−8.6% vs last week

All papers · 876 With code · 26

Newest Most relevant

814 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 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 scenes when camera poses differ significantly from training views.
- 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 scene generation1 paper raises it · 1 addresses it
Generated 3D scenes often lack physically plausible object interactions and spatial compatibility.
- Tetris3D: 3D Scene Generation With Objects That Fit Togetherraises · addresses
Dense correspondence matching beyond rigid motion1 paper raises it · 1 addresses it
Existing correspondence methods fail 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 geometry.
- Pumpire: Unified Benchmark for Metric Distance Estimationraises · addresses

Named by an AI from these papers' abstracts.