# Gaussian Splatting

Digitizing a plant in 3D usually takes tens to hundreds of calibrated photographs, or costly laser scanning and robotic capture, while single-image generators struggle to align their output back to the input photo. Plant Orbiter uses a video diffusion model to synthesize an orbit video around the plant from one frontal image, then fits 3D Gaussian splats using the known camera path, skipping structure-from-motion camera recovery. Across PlantDreamer, Botany-Bot and PLANesT, its 3D F-score leads the compared methods on every tested dataset, its input-view IoU reaches 0.552 and 0.556 on PlantDreamer and Botany-Bot, and height measured on the GrowSplat time series gives a mean absolute percentage error of 5.9%.

Digitizing a plant in 3D usually takes tens to hundreds of calibrated photographs, or costly laser scanning and robotic capture, while single-image generators struggle to align their output back to the input photo. Plant Orbiter uses a video diffusion model to synthesize an orbit video around the plant from one frontal image, then fits 3D Gaussian splats using the known camera path, skipping structure-from-motion camera recovery. Across PlantDreamer, Botany-Bot and PLANesT, its 3D F-score leads the compared methods on every tested dataset, its input-view IoU reaches 0.552 and 0.556 on PlantDreamer and Botany-Bot, and height measured on the GrowSplat time series gives a mean absolute percentage error of 5.9%.

Trained anchor-based Gaussian-splat scenes stay too large for phones and headsets, and existing post-hoc converters saturate early. This scheme treats a trained model as a read-only artifact, fits a per-scene hash-grid entropy context with per-channel Laplace distributions, and codes attributes into a Morton-ordered, progressively decodable bitstream. Across 13 scenes on three benchmarks, the post-hoc mode averages 6.0x compression at a -0.03 dB quality change and runs unchanged on third-party checkpoints.

Trained anchor-based Gaussian-splat scenes stay too large for phones and headsets, and existing post-hoc converters saturate early. This scheme treats a trained model as a read-only artifact, fits a per-scene hash-grid entropy context with per-channel Laplace distributions, and codes attributes into a Morton-ordered, progressively decodable bitstream. Across 13 scenes on three benchmarks, the post-hoc mode averages 6.0x compression at a -0.03 dB quality change and runs unchanged on third-party checkpoints.

Driving world action models explore candidate trajectories absent from logs during reinforcement-learning post-training, but logs store future video only for recorded trajectories, so those candidates lack matched visual supervision. SimVTA renders future video along candidate paths with a simulator that reconstructs real scenes via 3D Gaussian Splatting, giving prediction-independent targets, then selects high-reward, high-error futures by trajectory reward and video prediction error for flow-matching supervision while GRPO optimizes planning. On NAVSIM v1 it raises PDMS from 86.2 to 91.3, reaches 88.2 EPDMS on NAVSIM v2, and averages 38.8 HD-Score on HUGSIM.

Driving world action models explore candidate trajectories absent from logs during reinforcement-learning post-training, but logs store future video only for recorded trajectories, so those candidates lack matched visual supervision. SimVTA renders future video along candidate paths with a simulator that reconstructs real scenes via 3D Gaussian Splatting, giving prediction-independent targets, then selects high-reward, high-error futures by trajectory reward and video prediction error for flow-matching supervision while GRPO optimizes planning. On NAVSIM v1 it raises PDMS from 86.2 to 91.3, reaches 88.2 EPDMS on NAVSIM v2, and averages 38.8 HD-Score on HUGSIM.

Three-dimensional Gaussian reconstruction for building inspection no longer aims only at photorealistic views: with region and point-wise depth supervision, Gaussian centers are pulled toward physical surfaces, cutting depth error by 58.1% and 86.8% on synthetic UAV and smartphone-captured data, with a mean relative height-measurement error of 0.33%.

Three-dimensional Gaussian reconstruction for building inspection no longer aims only at photorealistic views: with region and point-wise depth supervision, Gaussian centers are pulled toward physical surfaces, cutting depth error by 58.1% and 86.8% on synthetic UAV and smartphone-captured data, with a mean relative height-measurement error of 0.33%.

RDGSplat introduces a two-pass framework that decodes a render-dedicated geometry from a frozen 3D foundation model by duplicating pretrained decoders and optimizing them under photometric supervision alone, together with a target-pose conditioned adapter that reformulates the representation those decoders read, while the model's metric predictions remain unchanged. Across four benchmarks and three feed-forward backbones, it improves novel view synthesis, raising WM2.0 on RE10K from 20.918 to 24.266 dB with 205.5 M added trainable parameters, and the depth and pose the same model predicts are bit-exact.

RDGSplat introduces a two-pass framework that decodes a render-dedicated geometry from a frozen 3D foundation model by duplicating pretrained decoders and optimizing them under photometric supervision alone, together with a target-pose conditioned adapter that reformulates the representation those decoders read, while the model's metric predictions remain unchanged. Across four benchmarks and three feed-forward backbones, it improves novel view synthesis, raising WM2.0 on RE10K from 20.918 to 24.266 dB with 205.5 M added trainable parameters, and the depth and pose the same model predicts are bit-exact.

VolS-GS turns the spatial support of a Gaussian scene into an occupancy field on a coarse grid and runs a differentiable finite-volume light-transport solve on it, with a small network predicting per-Gaussian scattering and absorption coefficients so light can propagate through the object's interior; across three OLAT benchmarks it consistently improves relighting quality on held-out lights and views over GS3, SSS-GS, RNG, and SSD-GS.

VolS-GS turns the spatial support of a Gaussian scene into an occupancy field on a coarse grid and runs a differentiable finite-volume light-transport solve on it, with a small network predicting per-Gaussian scattering and absorption coefficients so light can propagate through the object's interior; across three OLAT benchmarks it consistently improves relighting quality on held-out lights and views over GS3, SSS-GS, RNG, and SSD-GS.

The work proposes Gabor primitives for MRI reconstruction, modulating each Gaussian envelope with a complex exponential so its spectral support can be placed at an arbitrary k-space location, and designs a two-basis low-rank temporal model separating geometry and signal-intensity dynamics; on 99 Cartesian (R=12, R=16) and 102 radial (R≈23) cardiac cine acquisitions, Gabor primitives achieve the highest PSNR and SSIM in all settings, improving over Gaussian primitives by +1.11/+0.72/+0.86 dB and over PICS by +2.34 dB on radial data, with a parameter ratio ρ<0.5 and continuous-resolution evaluation enabling 4× super-resolution.

The work proposes Gabor primitives for MRI reconstruction, modulating each Gaussian envelope with a complex exponential so its spectral support can be placed at an arbitrary k-space location, and designs a two-basis low-rank temporal model separating geometry and signal-intensity dynamics; on 99 Cartesian (R=12, R=16) and 102 radial (R≈23) cardiac cine acquisitions, Gabor primitives achieve the highest PSNR and SSIM in all settings, improving over Gaussian primitives by +1.11/+0.72/+0.86 dB and over PICS by +2.34 dB on radial data, with a parameter ratio ρ<0.5 and continuous-resolution evaluation enabling 4× super-resolution.