DrivingForward: Real-Time 3D Scene Reconstruction

• DrivingForward is a real-time feed-forward system that reconstructs 3D scenes using multi-view imagery and self-supervised learning.
• It employs integrated networks for pose, depth, and Gaussian prediction to generate accurate, dense scene representations.
• The framework enables end-to-end autonomous driving by fusing perception, planning, and control with minimal latency.

DrivingForward refers both to a novel feed-forward 3D scene reconstruction architecture for autonomous driving and, more generally, to the family of system methodologies and paradigms underlying forward progress, perception, motion synthesis, and longitudinal control in autonomous vehicles. The term encompasses multiple axes: real-time feed-forward reconstruction from multi-view imagery, end-to-end motion planning that produces stable forward trajectories, and the synthesis of digital environments for autonomous driving research and deployment. The evolution of DrivingForward reflects the transition of autonomous vehicles from modular, sequential control systems to architectures exhibiting tight integrative coupling across perception, prediction, planning, and control.

1. Feed-Forward 3D Scene Reconstruction: The DrivingForward Architecture

DrivingForward (Tian et al., 19 Sep 2024) denotes the first real-time feed-forward 3D Gaussian Splatting (3DGS) system targeted at ego-centric, vehicle-mounted surround camera setups. Unlike prior scene-optimized methods requiring per-scene fitting and explicit 3D supervision, it learns depth, pose, and 3D appearance primitives via a unified, self-supervised loss across entire driving datasets.

Pipeline Overview:

The model consists of three networks trained end-to-end:

• Pose Network ($\mathcal{P}$): Learns relative 6-DoF transformations from pairs of temporally or spatially adjacent images.
• Depth Network ($\mathcal{D}$): Predicts pixel-wise metric depth for each image in the surround-view.
• Gaussian Network ($\mathcal{G}$): Predicts for each pixel a 3D Gaussian primitive characterized by anisotropic scale, orientation (quaternion), opacity, and view-dependent color (via spherical harmonics).

At inference, each image passes through the depth and Gaussian networks, creating a dense cloud of pixel-aligned, anisotropic Gaussians unprojected into a unified vehicle-centric coordinate frame. A splatting renderer aggregates these to generate novel views or reconstruct the scene mesh.

Key Parameterization:

• Center: $\mu_k \in \mathbb{R}^3$
• Covariance: $\Sigma_k = R_k\,\text{diag}(s_k)\,R_k^\top$
• Opacity: $\alpha_k \in [0,1]$
• Spherical harmonics color: $c_k \in \mathbb{R}^{3\times K}$

Rendering:

Novel views are obtained in one forward pass, where rendering is realized by accumulating the contributions of Gaussians along each camera ray in depth order, with compositing via a front-to-back strategy.

2. Self-Supervised Losses and Training Paradigm

The DrivingForward training objective is fully self-supervised, relying on photometric consistency across spatially and temporally adjacent camera views, with no requirement for explicit depth or pose labels.

• Photometric Reprojection Loss:

For a target view and a source (neighboring) frame, project (using estimated pose/depth) the source into the target, and compute a joint SSIM and $L_1$ difference.

• Smoothness Loss:

Edge-aware regularization on the predicted depth map.

• Rendering Loss:

For the predicted 3D Gaussian scene, render a target novel view and minimize $L_2$ plus perceptual (LPIPS) distance to ground-truth imagery.

The global loss is: $$\mathcal{L} = \mathcal{L}_{\mathrm{loc}} + \lambda_{\mathrm{render}}\mathcal{L}_{\mathrm{render}}$$ where $\mathcal{L}_{\mathrm{loc}}$ fuses photometric and smoothness terms, and $\lambda_{\mathrm{render}}$ weighs the rendered image match.

3. Real-Time Inference and System Performance

Feed-Forward Novel View Synthesis:

• Each input surround-view image undergoes independent depth estimation and Gaussian primitive prediction.
• All Gaussians (from all views and even multiple time steps) are pooled and rendered in a single GPU rasterization over 0.6 s for a standard six-camera rig at $352\times 640$ resolution.

Comparative Benchmarks on nuScenes:

Method	PSNR↑	SSIM↑	LPIPS↓	Time (s)
MVSplat	22.83	0.629	0.317	1.39
pixelSplat	25.00	0.727	0.298	2.95
DrivingForward	26.06	0.781	0.215	0.63
3DGS (scene opt)	19.57	0.599	0.465	540

Ablation studies indicate the necessity of joint training, accurate depth-pose coupling, and proper feature sharing: removing any results in significant degradation ($\mathrm{PSNR}\downarrow$ by $>4$).

4. DrivingForward in the Broader AV End-to-End Planning Stack

System Integration:

• DrivingForward is agnostic to scenario overlap and supports any number of surround images, crucial for the variable camera layouts of real-world fleets.
• As a mapping backbone, it enables real-time scene understanding for higher-level planning modules (trajectory generation, collision prediction) to operate on physically accurate, dynamically synchronized 3D reconstructions without expensive pre-processing.

Contrast with Traditional Pipelines:

• Scene-optimized NeRF/3DGS approaches demand per-scene/fleet calibration and high-frequency LiDAR labels, limiting operational scalability.
• DrivingForward’s feed-forward, zero optimization approach permits deployment on live vehicles or simulator-in-the-loop settings, with no need for per-ride adaptation.

5. Extensions and Related Methodologies in DrivingForward

While the term DrivingForward originated with feed-forward scene reconstruction, several state-of-the-art end-to-end (E2E) autonomous driving frameworks extend the "driving forward" philosophy into planning, control, and risk modeling.

Selected Examples:

• Unified Diffusion Planners:

DiffAD models the entire perception-prediction-planning loop as conditional BEV image generation, achieving state-of-the-art driving scores and forward progress robustness in closed-loop CARLA trials (Wang et al., 15 Mar 2025).

• Transformers for Forward Driving:

DriveTransformer replaces sequential task pipelines with a stack of unified attention layers, achieving 35% success and 0.40 m average trajectory error in Bench2Drive forward-driving benchmarks (Jia et al., 7 Mar 2025).

• Energy-Based Risk Flows:

FlowDrive introduces explicit risk repulsion and lane attraction fields into BEV, yielding interpretable, gradient-directed anchor adaptation for safe forward motion (Jiang et al., 17 Sep 2025).

Implications:

DrivingForward (in the system-design sense) denotes the broader trend toward architectures allowing joint optimization, real-time inference, and reduced reliance on brittle, heavily engineered submodules for stable forward navigation under complex traffic.

6. Practical Impact and Open Problems

DrivingForward architectures materially improve map-building, semantic understanding, and trajectory forecasting for AVs in urban and highway regimes. Demonstrated improvements include:

• Robust scene estimation in the presence of sparsely overlapped or minimally calibrated sensors.
• Reduced system latency through absence of test-time optimization.
• Efficient scaling to large fleets and variable hardware.

Challenges remain in extending these methods beyond visual modalities, integrating directly with control stacks, and achieving interpretable, certifiable safety for regulatory approval. Additionally, addressing rare long-tail events, multi-agent occlusions, and inference-time efficiency in resource-constrained embedded deployments are ongoing areas of research (Wang et al., 15 Nov 2024).

7. Conclusion

DrivingForward refers both to a specific technical advance—real-time feed-forward 3D Gaussian Splatting for scene reconstruction—and, more broadly, to a systems-theoretic approach in autonomous driving prioritizing end-to-end learnability, data-driven robustness, and operational scalability in forward-driving maneuvers. As a mapping and representation layer, DrivingForward catalyzes the tight coupling of perception, planning, and control required for Level 4–5 autonomy, while remaining deployable in real-time, large-scale fleet settings (Tian et al., 19 Sep 2024, Jiang et al., 17 Sep 2025, Wang et al., 15 Mar 2025, Jia et al., 7 Mar 2025, Wang et al., 15 Nov 2024).