DrivingGaussian++: Dynamic Scene Reconstruction

• DrivingGaussian++ is a Gaussian-splatting framework that reconstructs dynamic autonomous driving scenes with both photorealistic surround-view synthesis and training-free editing.
• It leverages incremental static 3D Gaussians, a dynamic Gaussian graph, and LiDAR priors to ensure detailed, consistent scene reconstruction and robust object handling.
• The system integrates LLM-driven motion generation to simulate plausible object trajectories, enabling intuitive edits like texture changes, weather simulation, and object manipulation.

DrivingGaussian++ is a Gaussian-splatting framework for realistic reconstructing and controllable editing of surrounding dynamic autonomous driving scenes. It models the static background using incremental 3D Gaussians and reconstructs moving objects with a composite dynamic Gaussian graph, integrates a LiDAR prior for detailed and consistent scene reconstruction, and supports training-free controllable editing for dynamic driving scenes, including texture modification, weather simulation, and object manipulation. The framework also integrates LLMs to automatically generate dynamic object motion trajectories and enhance their realism during the optimization process, positioning it at the intersection of dynamic scene reconstruction, surround-view synthesis, and editable driving-scene simulation (Xiong et al., 28 Aug 2025).

1. Definition and system scope

DrivingGaussian++ is defined by a dual objective: realistic reconstruction of dynamic driving scenes and controllable editing of those scenes after reconstruction. In contrast to Gaussian-splatting systems that focus only on rendering quality, the framework explicitly targets both photorealistic surround-view synthesis and editable simulation. This is evident in its coupling of incremental static reconstruction, object-centric dynamic modeling, LiDAR-guided optimization, and post hoc editing operators such as object insertion, object removal, weather simulation, and texture modification (Xiong et al., 28 Aug 2025).

A common misunderstanding is to treat DrivingGaussian++ as only a renderer. The method is broader: it supports training-free controllable editing for dynamic driving scenes, and it incorporates LLMs so that inserted or manipulated objects can receive automatically generated motion trajectories. A plausible implication is that the framework is intended not only for offline reconstruction, but also for scenario diversification and controllable simulator generation, especially in multi-view autonomous-driving settings (Xiong et al., 28 Aug 2025).

2. Static background and dynamic foreground representation

The static scene is represented by incremental static 3D Gaussians. Each static-scene Gaussian $G_s$ is an anisotropic ellipsoid with mean $\mu \in \mathbb{R}^3$, covariance $\Sigma \in \mathbb{R}^{3 \times 3}$, spherical-harmonic color coefficients $C(r,g,b)$, and opacity $\alpha \in [0,1]$. Its density at a query point $l \in \mathbb{R}^3$ is

$$p(l \mid \mu, \Sigma) = \exp\!\left(-\tfrac12 (l-\mu)^\top \Sigma^{-1} (l-\mu)\right).$$

The driving sequence is split into $N$ overlapping bins along the LiDAR depth range, and in bin $b$ only those Gaussians whose projected images lie in the current frames are refined. When merging bin $b+1$ into bins $\mu \in \mathbb{R}^3$0, the 3D centers are united as

$\mu \in \mathbb{R}^3$1

Color compositing across bins follows front-to-back alpha blending,

$\mu \in \mathbb{R}^3$2

after which differentiable Gaussian splatting projects the Gaussians into each camera view $\mu \in \mathbb{R}^3$3, weighting by view-dependent spherical harmonics (Xiong et al., 28 Aug 2025).

The dynamic foreground is modeled by a composite dynamic Gaussian graph,

$\mu \in \mathbb{R}^3$4

where $\mu \in \mathbb{R}^3$5 are object instances, $\mu \in \mathbb{R}^3$6 are per-object Gaussians, $\mu \in \mathbb{R}^3$7 are rigid-body transforms from object frame to world frame, $\mu \in \mathbb{R}^3$8 are Gaussian centers over time, $\mu \in \mathbb{R}^3$9 are object-bounding-box orientations, and $\Sigma \in \mathbb{R}^{3 \times 3}$0 is the set of time indices. Each dynamic-object Gaussian $\Sigma \in \mathbb{R}^{3 \times 3}$1 in object-local coordinates is mapped to world space by

$\Sigma \in \mathbb{R}^{3 \times 3}$2

For overlapping objects in the same view, the nearer object's opacity is adjusted as

$\Sigma \in \mathbb{R}^{3 \times 3}$3

and the final global field is the union of static and dynamic Gaussians,

$\Sigma \in \mathbb{R}^{3 \times 3}$4

This decomposition suggests that DrivingGaussian++ treats foreground motion as object-structured rather than as a scene-wide deformation field, with explicit mechanisms for position and occlusion handling (Xiong et al., 28 Aug 2025).

3. LiDAR prior, optimization objective, and surround-view rendering

LiDAR is used as an explicit prior during initialization and optimization. The method merges multi-sweep LiDAR, projects each point $\Sigma \in \mathbb{R}^{3 \times 3}$5 to every camera $\Sigma \in \mathbb{R}^{3 \times 3}$6 at time $\Sigma \in \mathbb{R}^{3 \times 3}$7 by

$\Sigma \in \mathbb{R}^{3 \times 3}$8

and picks the shortest-distance projection to assign color. Camera poses and LiDAR are then aligned through dense bundle adjustment to minimize reprojection error. During Gaussian optimization, a LiDAR-supervision term is added,

$\Sigma \in \mathbb{R}^{3 \times 3}$9

where $C(r,g,b)$0 are measured LiDAR point positions and $C(r,g,b)$1 are Gaussian centers. The full objective is

$C(r,g,b)$2

with

$C(r,g,b)$3

and $C(r,g,b)$4, described as a robust Huber-like penalty (Xiong et al., 28 Aug 2025).

Surround-view synthesis uses differentiable Gaussian splatting. Each 3D Gaussian projects to an image as a 2D elliptical splat with image-plane covariance

$C(r,g,b)$5

where $C(r,g,b)$6 is world-to-camera and $C(r,g,b)$7 is the projection Jacobian. Colors are shaded via spherical harmonics and composited in depth-sorted order with alpha blending. The pipeline gathers visible Gaussians from $C(r,g,b)$8, projects $C(r,g,b)$9 via the image-plane covariance relation, shades and composites them into a novel view, and backpropagates $\alpha \in [0,1]$0 to refine $\alpha \in [0,1]$1 end-to-end in approximately $\alpha \in [0,1]$2 iterations (Xiong et al., 28 Aug 2025).

The LiDAR prior is central rather than auxiliary. Relative to vision-only dynamic Gaussian systems such as VDG, which can work with only RGB image input and integrates self-supervised VO for pose and depth initialization, DrivingGaussian++ uses LiDAR to improve scene consistency and geometric detail (Li et al., 2024).

4. Training-free controllable editing

DrivingGaussian++ supports three editing mechanisms: texture modification, weather simulation, and object manipulation. These are described as training-free, and they leverage multi-view images and depth priors rather than requiring scene-specific retraining from scratch (Xiong et al., 28 Aug 2025).

For texture modification, one or more viewpoints are selected and rendered to produce an image $\alpha \in [0,1]$3 and depth $\alpha \in [0,1]$4. A 2D mask $\alpha \in [0,1]$5 is defined, and a 2D diffusion or in-painting model produces a target image $\alpha \in [0,1]$6. To avoid surface unevenness, the depth within the edited region is flattened as

$\alpha \in [0,1]$7

Each edited pixel $\alpha \in [0,1]$8 is then inverse-projected into 3D to generate a small set of new flat Gaussians $\alpha \in [0,1]$9, which are merged into the composite field and re-rendered (Xiong et al., 28 Aug 2025).

For weather simulation, the method instantiates particle Gaussian sets such as

$l \in \mathbb{R}^3$0

with positions sampled by

$l \in \mathbb{R}^3$1

colors $l \in \mathbb{R}^3$2, and scale constraint

$l \in \mathbb{R}^3$3

Each particle Gaussian is given a simple time-varying trajectory,

$l \in \mathbb{R}^3$4

For accumulation effects such as snow cover, image normals are computed from depth using Sobel operators,

$l \in \mathbb{R}^3$5

where

$l \in \mathbb{R}^3$6

A mask $l \in \mathbb{R}^3$7 is generated where the vertical component of $l \in \mathbb{R}^3$8 is large, inverse-projected to 3D, and deduplicated through a K-D tree and KNN pruning:

$l \in \mathbb{R}^3$9

These weather Gaussians are merged into $$p(l \mid \mu, \Sigma) = \exp\!\left(-\tfrac12 (l-\mu)^\top \Sigma^{-1} (l-\mu)\right).$$0 at every frame to simulate precipitation, fog drift, and related effects (Xiong et al., 28 Aug 2025).

For object manipulation, removal deletes all Gaussians inside a 3D bounding box and fills holes using neighboring Gaussians within distance $$p(l \mid \mu, \Sigma) = \exp\!\left(-\tfrac12 (l-\mu)^\top \Sigma^{-1} (l-\mu)\right).$$1,

$$p(l \mid \mu, \Sigma) = \exp\!\left(-\tfrac12 (l-\mu)^\top \Sigma^{-1} (l-\mu)\right).$$2

Rendered masks are then inpainted in 2D and reprojected via depth into the 3D Gaussian field. Insertion uses a 3D-Gaussian foreground bank of pre-reconstructed or driven models, including cars, pedestrians, and animals, via 3DGS or DreamGaussian, with optional lighting adjustment via MCLight (Xiong et al., 28 Aug 2025).

5. LLM-driven trajectory generation and editable simulation semantics

A distinctive feature of DrivingGaussian++ is the use of an off-the-shelf LLM, exemplified by GPT-4, to make inserted objects move plausibly. The prompt is constructed from an initial position $$p(l \mid \mu, \Sigma) = \exp\!\left(-\tfrac12 (l-\mu)^\top \Sigma^{-1} (l-\mu)\right).$$3, a sky direction $$p(l \mid \mu, \Sigma) = \exp\!\left(-\tfrac12 (l-\mu)^\top \Sigma^{-1} (l-\mu)\right).$$4, and a textual motion description $$p(l \mid \mu, \Sigma) = \exp\!\left(-\tfrac12 (l-\mu)^\top \Sigma^{-1} (l-\mu)\right).$$5. The model returns a sequence of displacements,

$$p(l \mid \mu, \Sigma) = \exp\!\left(-\tfrac12 (l-\mu)^\top \Sigma^{-1} (l-\mu)\right).$$6

which are integrated through

$$p(l \mid \mu, \Sigma) = \exp\!\left(-\tfrac12 (l-\mu)^\top \Sigma^{-1} (l-\mu)\right).$$7

These time-varying transforms $$p(l \mid \mu, \Sigma) = \exp\!\left(-\tfrac12 (l-\mu)^\top \Sigma^{-1} (l-\mu)\right).$$8 are then applied to the object Gaussians in the dynamic graph and re-rendered as part of $$p(l \mid \mu, \Sigma) = \exp\!\left(-\tfrac12 (l-\mu)^\top \Sigma^{-1} (l-\mu)\right).$$9 (Xiong et al., 28 Aug 2025).

This mechanism matters because it couples symbolic motion intent with geometric scene editing. The framework does not merely place a static asset into a reconstructed scene; it assigns a temporally evolving trajectory and incorporates that motion into the dynamic Gaussian composition. A plausible implication is that DrivingGaussian++ treats editability as a scene-level operation spanning geometry, appearance, and temporal evolution rather than a single-frame image-editing procedure (Xiong et al., 28 Aug 2025).

6. Empirical performance and relation to adjacent Gaussian-based driving systems

The reported evaluation covers reconstruction and editing. On nuScenes six-camera surround-view reconstruction, DrivingGaussian++ with LiDAR initialization (“Ours-L”) achieves $N$0, $N$1, and $N$2, compared to EmerNeRF at $N$3 and 3DGS at $N$4. On KITTI-360 monocular reconstruction, it reaches $N$5 SSIM, compared with DNMP at $N$6. For editing, it supports object insertion, object removal, weather, texture, and dynamic tasks in approximately $N$7 mins per scene, compared with $N$8–$N$9 mins for InstructNeRF2NeRF or ClimateNeRF. Additional quality indicators include object-insertion CLIP-direction similarity $b$0 versus $b$1 for Paint-by-Example, object-removal LPIPS $b$2 versus $b$3 for SD-inpainting, weather-edit CLIP-dir $b$4 versus $b$5 for UltraEdit, and texture-edit CLIP-dir $b$6 versus $b$7 for AnyDoor (Xiong et al., 28 Aug 2025).

Setting	Reference figures	DrivingGaussian++
nuScenes 6-cam reconstruction	EmerNeRF $b$8; 3DGS $b$9	Ours-L $b+1$0
KITTI-360 monocular reconstruction	DNMP $b+1$1	$b+1$2
Editing efficiency	InstructNeRF2NeRF or ClimateNeRF: $b+1$3–$b+1$4 mins	$b+1$5 mins per scene

A common source of confusion is that several recent autonomous-driving systems also use Gaussian primitives, but they target different layers of the stack. VDG is a pose-free dynamic Gaussian method that can work with only RGB image input and integrates self-supervised VO for pose and depth initialization (Li et al., 2024). Uni-Gaussians unifies camera and LiDAR simulation by using rasterization for image data and Gaussian ray-tracing for LiDAR data rendering (Yuan et al., 11 Mar 2025). LT-Gaussian addresses long-term map update for 3D-GS-based maps through multimodal Gaussian splatting, structural change detection, and targeted Gaussian-map update (Cheng et al., 3 Aug 2025). GaussianMap learns an adaptive Gaussian representation on the BEV plane for online HD map construction and vectorized map prediction (Lyu et al., 30 Jun 2026). GaussianFusion uses Gaussian-based multi-sensor fusion as an intermediate carrier for end-to-end autonomous driving and a cascade planning head for trajectory prediction (Liu et al., 27 May 2025). DrivingGaussian++ is distinct in combining dynamic reconstruction, LiDAR-guided surround-view synthesis, training-free controllable editing, and LLM-driven motion generation within a single framework (Xiong et al., 28 Aug 2025).

Within that broader literature, DrivingGaussian++ can be understood as a reconstruction-and-editing system rather than a map-construction method, a planner, or a long-term map updater. This suggests that its principal contribution lies in making dynamic driving scenes both photorealistically reconstructable and operationally editable, with explicit support for controllable scenario generation in autonomous-driving simulation (Xiong et al., 28 Aug 2025).