AdpSplit adaptively splits Gaussians using pixel-error statistics to reduce 3DGS training time by 9-22% without quality loss.
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Nerf: Representing scenes as neural radiance fields for view synthesis,
Canonical reference. 83% of citing Pith papers cite this work as background.
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representative citing papers
S2C-3D reconstructs complete high-fidelity 3D scenes from as few as 6-8 images by finetuning a diffusion model on scene data, applying consistency-conditioned sampling, and planning trajectories for full coverage.
Text Encoded Extrusions (TEE) lets LLMs generate and edit manifold 3D meshes by learning sequences of face extrusions from decomposed quadrilateral meshes.
Volumetric ergodic control optimizes spatial coverage for robots with physical volume using sample-based models, more than doubling coverage efficiency over point-based methods while preserving asymptotic guarantees and achieving 100% task completion.
BEVCALIB performs LiDAR-camera calibration from raw data by fusing camera and LiDAR bird's-eye view features with a novel feature selector and reports state-of-the-art accuracy on KITTI and NuScenes.
PG-3DGS couples 3D Gaussian Splatting with differentiable physics so that optimized shapes satisfy both visual fidelity and physical objectives such as pouring and aerodynamic lift, with real-world 3D-printed validation.
PropSplat uses optimized 3D Gaussians initialized on transmitter-receiver paths to achieve lower RMSE than NeRF2, GSRF, and WRF-GS+ on outdoor drive-test and indoor BLE datasets while enabling map-free RF reconstruction.
FreeOcc enables training-free open-vocabulary 3D occupancy prediction from RGB-D sequences by combining SLAM, dense Gaussian maps, off-the-shelf vision-language models, and probabilistic projection, achieving over 2x gains on benchmarks and zero-shot transfer to novel scenes.
RealLiFe optimizes multi-plane images with HSGD to deliver real-time light field reconstruction from sparse views, claiming 100x speedup over offline methods and 2 dB PSNR gain over online ones.
FalconApp converts iPhone video captures of rigid objects into on-device perception models for masks and 6-DoF poses using automatically labeled synthetic data from GSplat reconstruction, with 20-minute generation time and better accuracy than PnP on 4/5 tested objects.
Continuous trajectory representations of lithium-ion battery aging enable consistent knee-point detection and early remaining useful life predictions that remain robust across heterogeneous datasets.
A collaborative VR workflow with GenAI lets users merge prompts and creatively repurpose outputs to co-create 3D artifacts that narrate shared cultural heritage experiences.
GaNI combines NeuS geometry reconstruction with a light-position-aware inverse neural radiosity stage that adds implicit near-field modeling, surface angle loss, and roughness smoothness priors to recover reflectance parameters from co-located light-camera captures.
A systematic literature survey that categorizes deep learning architectures for point cloud classification, part segmentation, and semantic segmentation, evaluates them on benchmarks, and discusses innovations, limitations, and future directions.
Implicit neural representations enable stable, resolution-independent reconstruction of continuous environmental fields from sparse and irregular ecological data.
Gaussian primitives compress 3D Taylor-Green vortex flows at ratios over 1000x while preserving velocity but degrading enstrophy, with anisotropic extensions recovering small-scale vortical structures better than baseline or other variants.
A survey of 106 papers finds quality inspection dominates 3D reconstruction use in manufacturing at 40 percent of applications, with a shift toward hybrid sensor systems and a noted gap in unified frameworks.
citing papers explorer
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AdpSplit: Error-Driven Adaptive Splitting for Faster Geometry Discovery in 3D Gaussian Splatting
AdpSplit adaptively splits Gaussians using pixel-error statistics to reduce 3DGS training time by 9-22% without quality loss.
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Sparse-to-Complete: From Sparse Image Captures to Complete 3D Scenes
S2C-3D reconstructs complete high-fidelity 3D scenes from as few as 6-8 images by finetuning a diffusion model on scene data, applying consistency-conditioned sampling, and planning trajectories for full coverage.
-
Learning to Build Shapes by Extrusion
Text Encoded Extrusions (TEE) lets LLMs generate and edit manifold 3D meshes by learning sequences of face extrusions from decomposed quadrilateral meshes.
-
Volumetric Ergodic Control
Volumetric ergodic control optimizes spatial coverage for robots with physical volume using sample-based models, more than doubling coverage efficiency over point-based methods while preserving asymptotic guarantees and achieving 100% task completion.
-
BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations
BEVCALIB performs LiDAR-camera calibration from raw data by fusing camera and LiDAR bird's-eye view features with a novel feature selector and reports state-of-the-art accuracy on KITTI and NuScenes.
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PG-3DGS: Optimizing 3D Gaussian Splatting to Satisfy Physics Objectives
PG-3DGS couples 3D Gaussian Splatting with differentiable physics so that optimized shapes satisfy both visual fidelity and physical objectives such as pouring and aerodynamic lift, with real-world 3D-printed validation.
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PropSplat: Map-Free RF Field Reconstruction via 3D Gaussian Propagation Splatting
PropSplat uses optimized 3D Gaussians initialized on transmitter-receiver paths to achieve lower RMSE than NeRF2, GSRF, and WRF-GS+ on outdoor drive-test and indoor BLE datasets while enabling map-free RF reconstruction.
-
FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction
FreeOcc enables training-free open-vocabulary 3D occupancy prediction from RGB-D sequences by combining SLAM, dense Gaussian maps, off-the-shelf vision-language models, and probabilistic projection, achieving over 2x gains on benchmarks and zero-shot transfer to novel scenes.
-
RealLiFe: Real-Time Light Field Reconstruction via Hierarchical Sparse Gradient Descent
RealLiFe optimizes multi-plane images with HSGD to deliver real-time light field reconstruction from sparse views, claiming 100x speedup over offline methods and 2 dB PSNR gain over online ones.
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FalconApp: Rapid iPhone Deployment of End-to-End Perception via Automatically Labeled Synthetic Data
FalconApp converts iPhone video captures of rigid objects into on-device perception models for masks and 6-DoF poses using automatically labeled synthetic data from GSplat reconstruction, with 20-minute generation time and better accuracy than PnP on 4/5 tested objects.
-
Continuous ageing trajectory representations for knee-aware lifetime prediction of lithium-ion batteries across heterogeneous dataset
Continuous trajectory representations of lithium-ion battery aging enable consistent knee-point detection and early remaining useful life predictions that remain robust across heterogeneous datasets.
-
"From remembering to shaping": Narrating Shared Experiences by Co-Designing Cultural Heritage Artifacts in Collaborative VR
A collaborative VR workflow with GenAI lets users merge prompts and creatively repurpose outputs to co-create 3D artifacts that narrate shared cultural heritage experiences.
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GaNI: Global and Near Field Illumination Aware Neural Inverse Rendering
GaNI combines NeuS geometry reconstruction with a light-position-aware inverse neural radiosity stage that adds implicit near-field modeling, surface angle loss, and roughness smoothness priors to recover reflectance parameters from co-located light-camera captures.
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A Systematic Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation
A systematic literature survey that categorizes deep learning architectures for point cloud classification, part segmentation, and semantic segmentation, evaluates them on benchmarks, and discusses innovations, limitations, and future directions.
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Implicit neural representations as a coordinate-based framework for continuous environmental field reconstruction from sparse ecological observations
Implicit neural representations enable stable, resolution-independent reconstruction of continuous environmental fields from sparse and irregular ecological data.
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Gaussian Field Representations for Turbulent Flow: Compression, Scale Separation, and Physical Fidelity
Gaussian primitives compress 3D Taylor-Green vortex flows at ratios over 1000x while preserving velocity but degrading enstrophy, with anisotropic extensions recovering small-scale vortical structures better than baseline or other variants.
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3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases
A survey of 106 papers finds quality inspection dominates 3D reconstruction use in manufacturing at 40 percent of applications, with a shift toward hybrid sensor systems and a noted gap in unified frameworks.
- FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles