FreeVS: Generative View Synthesis on Free Driving Trajectory

## Abstract

Abstract Existing reconstruction-based novel view synthesis methods for driving scenes focus on synthesizing camera views along the recorded trajectory of the ego vehicle.
Their image rendering performance will severely degrade on viewpoints falling out of the recorded trajectory, where camera rays are untrained.
We propose FreeVS, a novel fully generative approach that can synthesize camera views on free new trajectories in real driving scenes.
To control the generation results to be 3D consistent with the real scenes and accurate in viewpoint pose, we propose the pseudo-image representation of view priors to control the generation process.
Viewpoint transformation simulation is applied on pseudo-images to simulate camera movement in each direction.
Once trained, FreeVS can be applied to any validation sequences without reconstruction process and synthesis views on novel trajectories.
Moreover, we propose two new challenging benchmarks tailored to driving scenes, which are novel camera synthesis and novel trajectory synthesis, emphasizing the freedom of viewpoints.
Given that no ground truth images are available on novel trajectories, we also propose to evaluate the consistency of images synthesized on novel trajectories with 3D perception models.
Experiments on the Waymo Open Dataset show that FreeVS has a strong image synthesis performance on both the recorded trajectories and novel trajectories.

## 1 Introduction

Scene reconstruction and novel view synthesis (NVS) have gained special attention in embodied AI due to their potential to develop closed-loop simulations for embodied systems.
Recent advances have led to remarkable improvements in the reconstruction quality of general scenes using multi-pass and multi-view recordings.
However, reconstructing driving scenes presents distinct challenges due to the sparse observations inherent in their less controlled, real-world recording conditions.

Unlike general scene reconstruction settings, which typically leverage excessive views surrounding the scene, driving scene reconstruction generally only has access to image views along the single-pass ego driving trajectory.
This limitation raises an important question: *How well does driving scene reconstruction perform for novel viewpoints outside the recorded trajectory?*

Currently, existing driving scene NVS works(Guo et al., ; Wu et al., ; Xie et al., ; Turki et al., ; Yang et al., ; Zhou et al., ; Chen et al., ; Yan et al., ) only evaluate their image rendering quality along the recorded trajectory, leaving this question largely unanswered.
As shown in Fig. 1, the quality of rendering results of existing representative NVS methods degrades drastically when the rendering camera moves away from its recording trajectory.
This is because, in driving scenes, recorded camera viewpoints are sparse in 3D space and homogeneous in their positions along the recorded trajectories.
The sparsity and homogeneity of recorded camera views cause the camera rays shooting from the camera centers on novel trajectories largely untrained.

We propose FreeVS to address this issue, which is a fully generative NVS method that can synthesize high-quality camera views inside and beyond the recorded driving trajectory.
We face two core challenges when building the FreeVS .
The first challenge is accurately controlling the camera poses while maintaining the 3D geometry consistency of the generated views.
Although previous diffusion-based methods(Wang et al., ; Lu et al., ; Hu et al., ; Wang et al., ; Yang et al., ) are capable of controlling the camera motion in a coarse trajectory, their control precision is far from enough for safety-critical simulation purposes.
The second challenge is the ground truth images in the novel trajectories are unavailable, making it difficult to directly train a model to synthesize novel views beyond recorded trajectories.

To tackle the two challenges, the proposed FreeVS leverages pseudo-image representation, a sparse yet accurate representation of 3D scenes obtained through colored 3D points projection.
Specifically, for each existing view, we create its pseudo-image counterpart by projecting colored point clouds into this view.
Here the colored points can be easily obtained by projecting point clouds to any valid images.
In this way, we obtain training data pairs to train a generative model that can generate a real image from its pseudo-image counterpart.
Since we create the pseudo images using ground truth camera models, they contain sparse but highly accurate appearance and geometry, sidestepping the tough challenge of accurately controlling the camera poses.
At inference time, we could create a pseudo-image for a novel viewpoint beyond the recorded trajectory and then synthesize the novel view using the trained generative model.
This design greatly narrowed the gap between synthesizing views inside and beyond the recorded trajectory.

To reveal the practicality of FreeVS, we propose two challenging benchmarks for evaluating the performance of NVS methods in driving scenes, which is more practically meaningful than the conventional evaluation on the recorded trajectories.
(i) On the recorded trajectories, we propose the novel camera synthesis benchmark.
Instead of evaluating synthesis results on test frames sampled at intervals from video sequences (i.e. novel frame synthesis), we propose to drop all images of a certain camera view (e.g. the front-side view) in the whole trajectory and synthesize the images of the dropped camera view.
(ii) We further propose the novel trajectory synthesis benchmark.
With no ground truth images available on novel trajectories, we propose to evaluate the geometry consistency of synthesized views through the performance of off-the-shelf 3D detectors.
The experiments on the Waymo Open Dataset (WOD) demonstrate that FreeVS outperforms previous NVS methods by a large margin in the two more practical benchmarks as well as in the traditional novel frame synthesis benchmark.

Our contributions are summarized as follows:

- 1.
We propose FreeVS, a fully generative view synthesis method for driving scenes that generate high-quality 3D-coherent novel views both for recorded and novel trajectories without time-cost reconstruction.
- 2.
We devise two new benchmarks for evaluating driving NVS methods on novel trajectories beyond recorded ones.
- 3.
Experiments on WOD show that FreeVS achieves leading performance on synthesizing camera views inside and beyond the recorded trajectory.

## 2 Related Work

### 2.1 Novel View Synthesis through reconstruction

Recently, rapid progress has been achieved in novel view synthesis through 3D reconstruction and radiance field rendering.
Neural Radiance Fields (NeRF)  (Mildenhall et al., ) utilizes multi-layer perceptrons to represent continuous volumetric scenes and achieve a breakthrough in rendering quality.
Many works have extended NeRF to unbounded, dynamic urban scenes (Tancik et al., ; Barron et al., ; Ost et al., ; Rematas et al., ; Turki et al., ; Lu et al., ; Guo et al., ; Liu et al., ; Wu et al., ; Xie et al., ; Turki et al., ; Yang et al., ; Wang et al., ; Ost et al., ; Tonderski et al., ).
Authors of MapNeRF (Wu et al., ) noticed the problem of NeRF in generating extrapolated views and proposed incorporating map priors to guide the training of radiance fields.
3D Gaussian Splatting (Kerbl et al., ) (3D GS) models scenes with numerous 3D Gaussians. Under this explicit representation, 3D GS can model scenes with significantly fewer parameters, while achieving faster rendering and training with splat-based rasterization.
3D GS is originally designed for static and bounded scenes.
Recently, some researchers have extended 3D GS to dynamic scenes (Luiten et al., ; Wu et al., ; Yang et al., ; ) and driving scenes (Zhou et al., ; Chen et al., ; Yan et al., ).
HUGS (Zhou et al., ) further jointly model the geometry, appearance, motion, and semantics in 3D scenes for better scene understanding.

### 2.2 Novel View Synthesis through Generation

Novel view synthesis through image generation has greatly benefitted from the advancements in diffusion models(Ho et al., ; Song et al., ; Rombach et al., ; Blattmann et al., ). Zero-1-to-3(Liu et al., ) and ZeroNVS(Liu et al., ) generate novel views with a diffusion process conditioned on the reference image and the target camera pose embedded as a text embedding. GeNVS(Chan et al., ) condition the diffusion process on volume-rendered feature images.
Reconfusion(Wu et al., ) uses the diffusion model to refine images rendered by the reconstruction model as extra supervision to the reconstruction process.
Similarly, RealFusion(Melas-Kyriazi et al., ) uses a diffusion model to provide an extra perspective view for object-centric reconstruction.
Yu et al. () use the Stable Video Diffusion model(Blattmann et al., ) to iteratively refine the rendered video along a novel camera trajectory based on the partial image wrapped from the reference view to the target view.
Most of the previous novel view synthesis through generation works are designed for object-centric(Liu et al., ; Chan et al., ; Wu et al., ; Melas-Kyriazi et al., ; Yu et al., ) or indoor(Liu et al., ; Chan et al., ; Wu et al., ; Yu et al., ) scenes.
For driving scenes, Yu et al. () proposes SGD which generates novel views with a diffusion process conditioned on reference images and depth maps in the target view. However, SGD still only synthesizes camera views along the recorded trajectory of the ego vehicle. Moreover, different from SGD which relies on the 3DGS model, FreeVS is a fully generative model with performance comparable to reconstruction models even on recorded trajectories.

## 3 FreeVS

We introduce the detailed design of our proposed FreeVS in this section.
We summarize the algorithm pipeline of FreeVS in Fig. 2.

Overview of FreeVS.
FreeVS is a fully generative model that synthesizes new camera views on novel trajectories based on observations of 3D scenes from recorded trajectories.
FreeVS is implemented as a conditional video diffusion model.
To ensure the model generates views from accurate viewpoints with consistent appearance attributes and 3D geometries as the real 3D scene, we formulate all essential priors regarding the 3D scene as pseudo-images to control the diffusion process.
Based on view prior conditions, FreeVS is learned by denoising noised target views at training time and synthesizing target views from pure noise at inference time.

### 3.1 View priors for view generation

#### Unified view prior representation.

One major challenge of generative novel view synthesis is to ensure the generated images are consistent with the priors in the novel view.
Here the view priors include the observed colors, 3D geometry, and camera pose of this view.
However, the different types of priors are in totally different modalities, posing a significant challenge for diffusion models to precisely encode them.
For example, as discussed in Sec. 1, diffusion models cannot precisely control the camera motions (i.e., poses).
To tackle this challenge, we propose a pseudo-image representation that unifies all types of view priors in one modality.
Pseudo-images are obtained through colored point cloud projection.
Specifically, for each frame in a driving sequence, we first merge LiDAR points across the nearby $r$ frames.
LiDAR points on moving objects will be merged along the moving trajectory of the object.
Finally, we project the merged and colored LiDAR point cloud into the target camera viewpoints as pseudo-images.
In this way, we encode color information, geometry information, and the view pose into a unified pseudo-image, largely facilitating the learning of generative models.

Compared with directly providing reference images and viewpoint transformations to the diffusion process, the pseudo-image representation greatly simplified the optimization objective of the generative model: With the former inputs, the model is required to have a correct understanding of the 3D scene geometry as well as the transformation of viewpoint to generate a correct view based on the reference image.
In contrast, with pseudo-image as input, FreeVS only needs to recover target views based on sparse valid pixels, which is more akin to a basic image completion task.
The simplification of the training objective greatly enhances the model’s robustness to unfamiliar viewpoint transformations, since the generated image is completed from sparse but geometrically accurate pixel points.

Viewpoint transformation simulation. Another challenge of novel view generation on new trajectories stems from the absence of ground truth views beyond recorded trajectories.
We can only train the generative model on recorded trajectories, where the diversity of viewpoint transformations is extremely limited.
For example, we have no access to the training sample where the frontal camera is moved laterally.
However, such viewpoint transformation is essential for synthesizing views on novel trajectories at inference time.
This brings a significant gap between training and inference for the generative model.
Moreover, we propose the viewpoint transformation simulation with pseudo-images.
At training time, we sample color and LiDAR priors from frames mismatched with the training image frames. That is to say, we force the generative model to recover current camera views based on observations from nearby frames.
Through this, we simulate the camera movement in each direction as a strong data augmentation on pseudo-image priors.
For example, as the ego vehicle moves along its heading direction, the side cameras are actually moving to their front or right,
Therefore although we have no access to the training data where the front camera is moved laterally, we can still simulate lateral camera movement by training FreeVS on side-views with mismatched observation-supervision frames.

### 3.2 Diffusion model for NVS

Figure: Figure 2: Method pipeline of FreeVS. We propose to encode the view priors in driving scenes including appearance, 3D geometry, and pose of target viewpoints all in one modality as the pseudo-images. Best viewed in color. The diffusion model is trained to synthesize target views solely based on the unified pseudo-image priors.
Refer to caption: x2.png

Training of FreeVS . In each training iteration of FreeVS , we randomly sample a colored LiDAR point cloud sequence ${\bf p}=(p_{1},...p_{n})$ from the driving scene dataset.
${\bf p}$ is a sequence of colored point clouds, each point cloud frame $p_{i}\in\mathbb{R}^{N_{i}\times 6}$ contains a set of 6-dimension 3D points.
3D points are recorded with their positions in the world reference frame and their visible colors.
From the driving sequence, we also sample a target camera viewpoint sequence ${\bf v}=([v_{1}^{1},...,v_{1}^{m}],...,[v_{n}^{1},...,v_{n}^{m}])$ of $n$ frames and $m$ surrounding cameras. Each camera parameter $v_{i}$ stands for the intrinsics and extrinsics of a camera viewpoint.
For a viewpoint in the target video with camera parameter $v_{i}^{j}$, we project the colored LiDAR point cloud $p_{i}$ into the viewpoint as pseudo-image $s_{i}^{j}=\text{Proj}(p_{i},v_{i}^{j})$.
The training target of FreeVS in each iteration is to recover the target images at sampled viewpoints based on the pseudo-image sequence ${\bf s}=([s_{1}^{1},...,s_{1}^{m}],...,[s_{n}^{1},...,s_{n}^{m}])\in\mathbb{R}
^{n\times m\times 3\times H\times W}$.

During the training process of FreeVS , the ground truth camera views ${\bf x}\in\mathbb{R}^{n\times m\times 3\times H\times W}$ is also sampled along the viewpoint sequence ${\bf v}$.
The ground truth camera views are encoded as target video latent representation $\mathcal{E}_{\text{VAE}}({\bf x})={\bf y}\in\mathbb{R}^{n\times m\times c
\times h\times w}$ through an frozen VAE encoder.
Then We have the diffused inputs ${\bf y}_{r}=\alpha_{\gamma}{\bf y}+\sigma_{\gamma}{\bf\epsilon},{\bf\epsilon}
\sim\mathcal{N}({\bf 0},\bm{I})$, here $\alpha_{\tau}$ and $\sigma_{\tau}$ is noise schedule at the diffusion time step $\tau$.
We also encode the pseudo-images into the latent representation $\mathcal{E}_{p}({\bf s})={\bf z}\in\mathbb{R}^{n\times m\times c\times h\times
w}$ with a 2D encoder trained simultaneously with the diffusion model.
We concatenate ${\bf y}_{r}$ and ${\bf z}$ as the input ${\bf k}\in\mathbb{R}^{n\times m\times 2c\times h\times w}$ to the diffusion model to predict the noise upon ${\bf y}$.
We have a denoising model ${\bf f}_{\theta}$ with parameters $\theta$ that take ${\bf y}_{r}$,${\bf z}$ as inputs and optimized by minimizing the following denoising objective:

$$ $\mathbb{E}_{\mathbf{k},\tau\sim p_{\tau},\epsilon\sim\mathcal{N}(\mathbf{0}, \bm{I})}[\|\epsilon-{\bf f}_{\theta}({\bf k};{\bf c},\tau)\|_{2}^{2}],$ (1) $$

Where ${\bf c}$ is the description conditions generated by encoding the reference camera views with an off-the-shelf CLIP-vision model(Radford et al., ), following the convention of diffusion models. $p_{\tau}$ is a uniform distribution over the diffusion time $\tau$.

Synthesizing views on novel trajectories with FreeVS . During the inference process of FreeVS , we project the colored LiDAR points in each frame into the targeted camera poses to generate pseudo-image sequence for image synthesis.
The diffusion model is fed with pure noise latents concatenated with pseudo-image latents.
The diffused latent will be decoded as synthesized views through an off-the-shelf VAE decoder $\mathcal{D}_{\text{VAE}}$.

### 3.3 Evaluating NVS on novel camera and novel trajectory synthesis

To thoroughly demonstrate the view generalization capability of our FreeVS , which can truly meet the demands of closed-loop embodied simulation, we present a comprehensive discussion of evaluation benchmarks for novel view synthesis in driving scenes. Fig. 3 illustrates this: panels (a) and (b) summarize existing evaluation benchmarks, while panels (c) and (d) introduce our two new challenging NVS benchmarks.

Evaluating NVS on recorded trajectories. All current NVS works for driving scenes evaluate their NVS performance on test frames sampled periodically along the recorded trajectory.
Some previous driving scene NVS methods, such as Street Gaussians(Yan et al., ), NSG(Ost et al., ), and Mars(Wu et al., ), evaluate their performance with only front camera views considered, as illustrated in Fig. 3(a).
Other NVS methods take the multi-view cameras into consideration as illustrated in Fig. 3(b), such as DrivinGaussian(Zhou et al., ),
PVG(Chen et al., ), EmerNerf(Yang et al., ), NeuRAD(Tonderski et al., ), S-Nerf(Xie et al., ), and SUDS(Turki et al., ).
All these two evaluation benchmarks sample test frames periodically along the trajectory, i.e. novel frame synthesis.
In such cases, camera views in test frames can be directly inferred from the adjacent frames, especially for datasets with a high video frame rate (e.g. 10Hz for the WOD dataset).
To provide a more challenging evaluation setting for driving scene NVS methods, we propose the novel camera synthesis benchmark as illustrated in Fig. 3(c).
Instead of periodically sampling test frames, we drop images collected by certain multi-view cameras throughout a driving sequence as test views.
For example, for a driving sequence in the WOD dataset, we provide NVS methods with the front, and side camera views as training views and evaluate the synthesis results on front-left and front-right views.
Under the novel camera synthesis benchmark, NVS methods are required to synthesize views on unseen camera poses, which places higher demands on accurately modeling the 3D scene.
We ensure in the validation sequences, most 3D contents in front-side cameras are observed in the front or side camera views along the ego trajectory.

Figure: Figure 3: Benchmarks for evaluating NVS methods in driving scenes. We conclude the previous NVS evaluation benchmarks for driving scenes as (a) and (b). We propose two novel evaluation benchmarks: the novel camera synthesis benchmark as in (c), and the novel trajectory synthesis benchmark as in (d). Best viewed in color.
Refer to caption: x3.png

Novel trajectory synthesis.
On test views sampled from the recorded trajectories, the ground truth camera images are available for evaluating the synthesized images with image similarity metrics including SSIM, PSNR, and LPIPS(Zhang et al., ).
Differently, in driving scenes, there are no ground truth images available on novel trajectories.
The Fréchet Inception Distance (FID)(Seitzer, ) metric can compare the overall image distribution between synthesized images on novel trajectories and ground truth images on recorded trajectories, but it can not assess the fidelity of the synthesized images to the 3D scenes at all.
In addition to qualitative visualization comparisons, we also propose the perceptual robustness evaluation to assess the geometry consistency performance of NVS methods on new ego trajectories.

In driving scenes, modern image-based 3D perception models have achieved high robustness.
As shown in Fig. 3(d), assuming an NVS method can synthesize views on a novel trajectory with ideal image quality, the perception model feed with synthesized views should still be able to produce accurate predictions on the novel trajectory.
With such an assumption, we believe that the performance of an off-the-shelf perception model on novel trajectories can reflect the quality of images synthesized by the NVS methods.
Under the novel trajectory synthesis benchmark, we feed the synthesized images and camera poses on the novel trajectory to an off-the-shelf 3D camera-based detector.
The detection results are evaluated with the longitudinal error tolerant mean average precision (LET-mAP)(Hung et al., ) metric on the WOD dataset.
For all NVS methods, we modify novel trajectories by laterally shifting the ego positions in each frames. We shift the trajectories by 1.0 m, 2.0 m, and 4.0 m and report the mean evaluated results as $\text{mAP}^{\text{LET}}_{1.0m}$,$\text{mAP}^{\text{LET}}_{2.0m}$, and $\text{mAP}^{\text{LET}}_{4.0m}$.

## 4 Experiments

In this section, we first introduce our experimental setup including datasets, evaluation benchmarks, method implementation details, and counterpart methods.
Then we provide the quantitative and qualitative experiment results.

Datasets.
We perform experiments on the WOD dataset(Sun et al., ). We select 12 driving sequences for evaluating NVS methods. We ensure that there is ample space on both sides of the ego vehicle in most frames of the selected sequence to simulate novel trajectories by lateral moving the ego vehicle. For each sequence, all 200 data frames in 10Hz are used.

Evaluation of NVS methods.
We compare FreeVS with NVS counterparts under all the experiment benchmarks shown in Fig. 3.
For the front-view or multi-view novel frame synthesis benchmark (Fig. 3(a) and (b)), we sample every fourth frame in driving sequences as test frames.
All the remaining frames are used for training NVS counterparts, or as input frames for FreeVS.
On, we report metrics including SSIM, PSNR, and LPIPS.
Under the novel camera synthesis benchmark, we reserve all the front-side camera views as test views and use the front and side camera views as train views throughout each sequence.
Note that for FreeVS which does not require scene reconstruction on validation sequences, we only take information from the train views to generate test views.

Moreover, we also evaluate NVS methods on novel trajectories with the FID score and the proposed perceptual robustness evaluation method.
We take MV-FCOS3D++(Wang et al., ), a basic yet strong multi-view camera-based 3D detector as our baseline detection model. We follow most of the settings of the official open-sourced implementation of MV-FCOS3D++.
We train MV-FCOS3D++ for 24 epochs on the WOD training set (except for the validation sequences in our experiments) to obtain the baseline detector.
Following (Wang et al., ), we initialize MV-FCOS3D++ from an FCOS3D++ checkpoint, which is also trained on the above training sequences.
We feed the camera views synthesized on novel trajectories to the baseline detector.
We report camera-based 3D detection metrics LET-mAP(Hung et al., ) on the vehicle class as $\text{mAP}^{\text{LET}}$.

Method details.
We implement the proposed FreeVS pipeline based on Stable Video Diffusion (SVD)(Blattmann et al., ).
We initialize the diffusion model from a pre-trained Stable Diffusion checkpoint(Rombach et al., ).
FreeVS is trained on the WOD training set, except for the selected validation sequences.
To generate pseudo-images, we accumulate colored LiDAR points across the adjacent $\pm 2$ frames of each frame.
If a 3D LiDAR point has more than one projected 2D point in multi-view images, the mean color of its projected image points will be recorded.
For viewpoint transformation simulation, we randomly sample the target viewpoint sequence starting from the adjacent $\pm 4$ frames of the first frame of the source point cloud sequence. We employ a ConVNext-T(Liu et al., ) backbone as the pseudo-image encoder. We train the model for 40,000 iterations with a batch size of 8 and video frame length $n=8$. Please refer to the appendix for more training details.

Novel view synthesis counterparts.
We compare our novel view synthesis method with the 3DGS(Kerbl et al., ), EmerNeRF(Yang et al., ), and Street Gaussians(Yan et al., ).
All counterpart methods are implemented based on their official implementation.
For 3DGS which is not initially designed for unbounded driving scenes, we largely increase its max training iterations for better convergence of the model. Please check the appendix for more implementation details on NVS counterparts.

### 4.1 SOTA comparison under the proposed challenging new benchmarks.

Novel camera synthesis. We first report the performance of NVS methods under the proposed multi-view novel camera synthesis benchmark in Table 1.
FreeVS achieves leading performance on all metrics by a large margin.
Previous NVS methods tend to render images with severe image distortion or massive unnatural artifacts when facing severe scene information loss on the target views, as shown in Fig. 4.
Meanwhile, FreeVS can generate camera views close to ground truth views based on limited 3D scene observations.

Novel trajectory synthesis. We also report the FID and perceptual robustness performance of NVS methods on novel trajectories in Table 2.
The proposed FreeVS outperforms previous NVS methods on almost all metrics with different lateral offsets applied to the viewpoints.
Compared to previous NVS methods, the proposed FreeVS has a very strong performance on the FID metric.
This is mainly because the proposed FreeVS is nearly free from image degradation and artifacts when synthesizing images on novel trajectories.
FreeVS also has the strongest $\text{mAP}^{\text{LET}}$ performance among all NVS methods, which indicates that as a generation-based method, FreeVS is of even higher fidelity to the 3D scene geometry compared with previous reconstruction-based methods when rendering views on novel trajectories. We also provide a visualization comparison example in Fig. 5.

While FreeVS relies on LiDAR point inputs, EmerNerf and Street Gaussians also rely on LiDAR depth supervision during their training process.
Therefore FreeVS did not gain any information advantages in our experiments.
Moreover, as a fully generative method, FreeVS does not require any scene reconstruction process when applied to validation sequences.
From this aspect, at inference time, FreeVS costs less computational resources even compared with 3DGS-based methods, which usually take 1-2 hours to model a validation sequence of 20s.

**Table 1: Comparison with NVS counterparts on novel camera synthesis. For all NVS methods, we use all front and side camera views as source views to synthesize the front-side camera views.**
|  | Front-side camera syntheising |  |  |
| --- | --- | --- | --- |
| Methods | SSIM$\uparrow$ | PSNR$\uparrow$ | LPIPS$\downarrow$ |
| 3D-GS(Kerbl et al., ) | 0.484 | 15.97 | 0.442 |
| EmerNerf(Yang et al., ) | 0.603 | 19.61 | 0.330 |
| StreetGaussian(Yan et al., ) | 0.531 | 17.35 | 0.397 |
| Ours | 0.628 | 20.70 | 0.283 |

**Table 2: Comparison with NVS counterparts on novel trajectories. The $y$ axis is defined lateral to the ego vehicle’s heading direction. †: performance of baseline detector on ground truth images.**
|  | $y\pm 0.0m$ | $y\pm 1.0m$ | $y\pm 2.0m$ | $y\pm 4.0m$ |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Methods | FID$\downarrow$ | $\text{mAP}^{\text{LET}}$$\uparrow$ | FID$\downarrow$ | $\text{mAP}^{\text{LET}}_{1.0m}$$\uparrow$ | FID$\downarrow$ | $\text{mAP}^{\text{LET}}_{2.0m}$$\uparrow$ | FID$\downarrow$ | $\text{mAP}^{\text{LET}}_{4.0m}$$\uparrow$ |
| GT† | - | 0.895 | - | - | - | - | - | - |
| 3D-GS | 34.79 | 0.729 | 52.07 | 0.605 | 61.16 | 0.581 | 86.21 | 0.452 |
| EmerNerf | 53.88 | 0.600 | 58.26 | 0.510 | 69.50 | 0.478 | 84.81 | 0.464 |
| StreetGaussian | 21.62 | 0.826 | 41.17 | 0.738 | 55.71 | 0.682 | 80.44 | 0.544 |
| Ours | 11.18 | 0.816 | 13.45 | 0.786 | 16.60 | 0.724 | 22.08 | 0.612 |

**Table 3: Comparison with NVS counterparts on recorded trajectories. We report the performance of NVS methods when only the front-view cameras are considered or when all multi-view cameras are considered. Reconstruction time cost and FPS are measured under the multi-view setting, with a single NVIDIA L20 GPU. †: we largely increased the max training iterations of 3D-GS for better performance in driving scenes.**
|  | Front View | Multi-view |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Methods | SSIM$\uparrow$ | PSNR$\uparrow$ | LPIPS$\downarrow$ | SSIM$\uparrow$ | PSNR$\uparrow$ | LPIPS$\downarrow$ | Reconstruction<br>time | FPS |
| 3D-GS(Kerbl et al., ) | 0.799 | 26.31 | 0.143 | 0.586 | 19.21 | 0.366 | 2-3h† | 61.2 |
| EmerNerf(Yang et al., ) | 0.869 | 30.28 | 0.155 | 0.689 | 24.68 | 0.347 | 2-3h | 0.2 |
| StreetGaussian(Yan et al., ) | 0.903 | 30.80 | 0.096 | 0.702 | 22.47 | 0.314 | 1-2h | 52.6 |
| Ours | 0.787 | 25.30 | 0.139 | 0.730 | 24.96 | 0.179 | - | 0.9 |

### 4.2 SOTA comparison on novel frame synthesis

We also report the performance of NVS methods under the traditional front-view novel frame synthesis or multi-view novel frame synthesis benchmark in Table 3.
The performance of previous NVS methods is strong when only the front-view camera is considered.
However, when it comes to the multi-view setting which is more aligned with the current autonomous driving scenes, the performance of previous NVS methods is surpassed by the proposed FreeVS by a large margin.
It is worth mentioning that in Table 3, all previous reconstruction-based NVS methods exhibit a significant performance drop when multi-view cameras are considered.
We think this is due to the increased number of training views, the expanded range of the visible 3D scene, and the rapidly changing content in lateral views, all of which make the convergence of reconstruction models more difficult.

**Table 4: Ablation study on view prior condition. We conduct a breakdown ablation on the proposed pseudo-image representation of view priors. The $y$ axis is defined lateral to the ego vehicle’s heading direction.**
|  | View priors | Encoders | Multi-view | $y\pm 2.0m$ |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | SSIM$\uparrow$ | PSNR$\uparrow$ | LPIPS$\downarrow$ | FID$\downarrow$ | $\text{mAP}^{\text{LET}}_{2.0m}$$\uparrow$ |  |  |
| (a) | full priors | 2D-Conv | 0.704 | 23.28 | 0.203 | 21.27 | 0.690 |
| (b) | w/o. color | 2D-Conv | 0.701 | 23.05 | 0.231 | 23.13 | 0.687 |
| (c) | w/o. LiDAR | 2D-Conv + MLP | 0.613 | 19.86 | 0.288 | 21.25 | 0.013 |
| (d) | w/o. projection | 2D-Conv + 3D + MLP | 0.609 | 19.88 | 0.284 | 21.32 | 0.028 |
| (e) | full priors | 2D-Attn | 0.706 | 23.27 | 0.202 | 21.25 | 0.692 |

**Table 5: Ablation study on viewpoint translation simulation. The $y$ axis is defined lateral to the ego vehicle’s heading direction.**
|  | Multi-view | $y\pm 2.0m$ |  |  |  |
| --- | --- | --- | --- | --- | --- |
| Temporal sampling window size | SSIM$\uparrow$ | PSNR$\uparrow$ | LPIPS$\downarrow$ | FID$\downarrow$ | $\text{mAP}^{\text{LET}}_{2.0m}$$\uparrow$ |
| - | 0.733 | 25.04 | 0.180 | 18.29 | 0.704 |
| $\pm 2$ frames | 0.734 | 25.04 | 0.179 | 18.24 | 0.710 |
| $\pm 4$ frames | 0.730 | 24.96 | 0.179 | 18.01 | 0.722 |
| $\pm 8$ frames | 0.717 | 24.82 | 0.188 | 18.09 | 0.720 |

### 4.3 Ablation Studies

Ablation on view prior condition.
We first ablate on the representation of view prior as conditions for the diffusion process, as shown in Table 4.
We apply a breakdown experiment on the pseudo-image representation.
Models are trained for 20,000 iterations.
We start by dropping the color information in the pseudo-image representation, represented by Table 4(b).
Dropping the color nearly does not affect the geometric accuracy of rendered results, but has a considerable impact on the image similarity metrics.
Then we experiment with dropping the LiDAR inputs (c), where the reference images and camera pose transformation matrix (from the reference view to the target view) are independently encoded by a VAE or MLP encoder. Under this setting, we found the diffusion model unable to accurately synthesize views on the target viewpoint. Most time, the model ignores the pose condition and moves the camera viewpoint by its familiar viewpoint transformation. (e.g. always move the frontal camera forward or backward, or move the side camera left or right.) Based on (c), we experiment with preserving all view prior inputs but do not unify them as pseudo-images (d). The LiDAR points are encoded as latents with a point cloud backbone(Yan et al., ). The experiment result shows the model fails at utilizing LiDAR inputs due to its significant gap with 2D images, the model trained under setting (d) has an identical performance as the mode trained under setting (c). Due to the wrong viewpoint of most generated images, trained models under settings (c) and (d) have extremely poor perceptual robustness performance. Through this observation, we can conclude that the pseudo-image representation greatly improved the overall quality and viewpoint controllability of images generation.
Finally, we compare encoding the pseudo-images with a 2D-Conv encoder or an 2D-attention encoder(Liu et al., ) with setting (e).

Ablation study on viewpoint transformation simulation.
Viewpoint transformation simulation aims at constructing source and target frame pairs from recorded sequences to simulate camera movement in various directions.
We report the results of ablation studies on viewpoint transformation simulation with pseudo-images in Table 5.
We report the performance of FreeVS under the multi-view novel frame synthesis setting and on novel trajectories generated by applying 2.0 m lateral offsets to the recorded trajectories.
As shown in Table 5, sampling target frames from adjacent $\pm 2$ or $\pm 4$ frames from the source frame can boost the view-synthesize performance of FreeVS on novel trajectories.
When the temporal sampling window size exceeds $\pm 4$ frames, the view-synthesize performance of FreeVS on the recorded trajectory will be negatively affected. We believe this is due to the large timestamp mismatch between view priors and target images hindering the model’s convergence.

### 4.4 Visualization comparison

We show visualization comparisons between NVS methods under the novel camera synthesis benchmark in Fig. 4, and under the novel trajectory synthesis benchmark in Fig. 5.

Figure: Figure 4: Visualization comparison on novel-camera synthesis benchmark. We show the front-side camera views synthesized from front and side camera views with NVS methods.
Refer to caption: x4.png

Figure: Figure 5: Visualization comparison on novel trajectories. We show the camera views synthesized by NVS methods on the original training viewpoint, viewpoint 2.0 m left of the original viewpoint, and viewpoint 2.0 m right of the original viewpoint.
Refer to caption: x5.png

### 4.5 Conclusion

We present FreeVS, a novel fully generative method for synthesizing camera views on free driving trajectory.
We propose the pseudo-image representation of view priors, which conveys accurate 3D scene geometry and viewpoint conditions through colored point projection.
The diffusion model is trained to synthesize target views solely based on pseudo-images.
In this paper, we fully discuss the evaluation benchmarks for driving scene NVS.
We propose two novel evaluation benchmarks including the novel camera synthesis benchmark and the novel trajectory synthesis benchmark.
We also propose the perceptual robustness evaluation method for assessing the performance of NVS methods on novel trajectories.
Experiments across several experiment benchmarks show that FreeVS achieves leading performance in synthesizing camera views inside or beyond recorded trajectories.

## Appendix A Appendix

### A.1 Implementation Details

Implementation details of FreeVS.
We employ a ConVNext-T(Liu et al., ) encoder to encode pseudo-images.
For training FreeVS, the diffusion model is initialized with Stable Diffusion checkpoints (Rombach et al., ).
We train the model for 40,000 iterations with a batch size of 8 and video frame length $n=8$. We use the AdamW optimizer (Kingma & Ba, ) with a learning rate $1\times 10^{-4}$.
During training time, we randomly drop the pseudo-image condition latent as well as the CLIP text description latent with a probability of 20%.
We enable the viewpoint transformation simulation with a probability of 50%.
During inference, we set the number of sampling steps as 25 and stochasticity $\eta$=1.0.
When synthesizing images on the existing trajectory, we set the classifier-free guidance (CFG)(Ho & Salimans, ) guidance scale to 1.0.
For synthesizing images on novel cameras and new trajectories, we enlarge the CFG guidance scale to 2.0 to strengthen the control of 3D prior conditions over the generated results.

Implementation details of NVS counterparts.
For 3DGS which is not initially designed for unbounded driving scenes, we optimize its performance by adjusting its hyperparameters, including setting the densification interval to 500 iterations, setting the opacity reset interval to 10000, and training the 3DGS models for 100000 iterations while densifying 3D Gaussians until 50000 iterations.
We also noticed that the Street Gaussians models have a convergence issue when training with all 200 frames of each sequence.
Therefore we split the validation sequences into two 100-frame sequences for training the Street Gaussians models, following its official configuration(Yan et al., ).
As 3DGS and EmerNerf suffer from high memory costs when training with high-resolution images, we resize the input images on WOD from a resolution of $1920\times 1280$ to $1248\times 832$ when training FreeVS and all NVS counterparts.
The pseudo-images are generated in the same resolution.
All experiments are conducted on NVIDIA L20 GPUs.

Figure: Figure A: Visualization comparison on moving objects. We compare the performance of NVS methods where moving objects are visible in camera views. In the shown case, our proposed method can generate more accurate images of vehicles driving in the opposite lane. Images are synthesized on training views.
Refer to caption: x6.png

Figure: Figure B: Visualization of the viewpoint transformation simulation. For a training sample, we show the (a) source camera view, (b) pseudo-image generated from current LiDAR observation, and (c) pseudo-image generated from previous LiDAR observation to simulate viewpoint transformation. We also show a validation case of (d) pseudo-image on the original viewpoint, (e) pseudo-image on the shifted viewpoint, and (f) generated image on the shifted viewpoint. Note the image areas with missing or overlapped 3D observation circled by red boxes in (c) and (f). The proposed viewpoint transformation simulation on pseudo-images can well-stimulate the insufficient 3D prior observations brought by the shift in viewpoint.
Refer to caption: x7.png

### A.2 Video comparison.

We present a video comparison of novel view sequences synthesized by NVS methods on the modified trajectory in a driving sequence.
Please check the video file submitted as supplementary material.

### A.3 Visualiation comparison on dynamic objects.

Without the scene reconstruction process, FreeVS is free from complex cross-frame optimization of dynamic objects.
FreeVS can synthesize images of dynamic objects in high accuracy, as shown in Fig.A.
In comparison, despite with specific design, current reconstruction-based NVS methods still suffer from dynamic object modeling.

### A.4 Visualization of the viewpoint transformation simulation.

Besides quantitatively ablating the impact of the proposed viewpoint transformation simulation with pseudo-images, we also provide a visualization case in Fig.B to illustrate its effect.

When facing the translation of viewpoints, pseudo images generated from the existing viewpoints cannot provide complete 3D priors, such as in areas circled by red boxes in Fig.B(e). To strengthen the robustness of FreeVS towards those patterns in pseudo-images, we propose to employ viewpoint transformation simulation with pseudo-images by generating pseudo-images in training views from mismatched LiDAR observations. As shown in Fig.B(c), we can stimulate the pseudo-image areas with insufficient 3D priors. The generative model trained with the proposed viewpoint transformation simulation with pseudo-images can render images of high quality when facing insufficient 3D prior inputs, as shown in Fig.B(f).

### A.5 Effect of classifier-free guidance.

Figure: Figure C: Visualization of the effect of classifier-free guidance (CFG). We show generation results with the same input pseudo-image and different CFG scales. The larger CFG scale is set, the stronger the generation result is constrained by the 3D prior condition.
Refer to caption: x8.png

As a diffusion model, FreeVS can use the classifier-free guidance (CFG) technique to adjust the control effect of input 3D prior condtions.
We show the impact of CFG with different CFG scales in Fig. C.

Figure: Figure D: Visualization comparison on the encoding of camera viewpoint condition. We qualitatively compare the image synthesis performance of the diffusion model with pseudo-image as input (Table 4(a)), or with the reference image, LiDAR points, and viewpoint transformation matrix as input (Table 4(d)). When feeding the model with pose transformation matrices, the diffusion models often fail to generate views on the targeted viewpoint, as shown in the second column.
Refer to caption: x9.png

### A.6 Visualization results of component ablation experiment on pseudo-image representation.

For the ablation setting (a) and (d) introduced in Sec.4.3, whose quantitative results are reported in Table 4, we present a visualization comparison in Fig.D.
Setting (a) is the baseline setting of feeding diffusion models with pseudo-image scene representations for view synthesis.
Under setting (d), we feed the diffusion model with the reference image, LiDAR point cloud, and the transformation matrix from the reference view to the target view.
As shown in Fig.D, model feed with pseudo-image can precisely synthesize image on the target viewpoint, while model feed with raw camera pose fails to follow the viewpoint condition.
Given that the diffusion model can only be trained on recorded trajectories, we found the diffusion model fed with reference images and viewpoint pose tends to overfit to the specific camera movement pattern in each camera position.
As shown in Fig.D, the model fed with raw 3D prior inputs will only move the frontal camera view forward or backward, ignoring the viewpoint pose condition.
This is due to the absence of a training sample where the frontal camera is moved laterally.
By modifying the novel view synthesis task as an image completion task based on the pseudo-image representation of 3D priors as well as applying the proposed viewpoint transformation simulation with pseudo-images, we can overcome this training data shortage.

### A.7 Validation Sequences

We list the selected 12 validation sequences from the WOD dataset here with their official individual file names:

- •
segment-10588771936253546636_2300_000_2320_000_with_camera_labels.tfrecord,
- •
segment-6242822583398487496_73_000_93_000_with_camera_labels.tfrecord,
- •
segment-16801666784196221098_2480_000_2500_000_with_camera_labels.tfrecord,
- •
segment-1191788760630624072_3880_000_3900_000_with_camera_labels.tfrecord,
- •
segment-10625026498155904401_200_000_220_000_with_camera_labels.tfrecord,
- •
segment-11846396154240966170_3540_000_3560_000_with_camera_labels.tfrecord,
- •
segment-18111897798871103675_320_000_340_000_with_camera_labels.tfrecord,
- •
segment-11017034898130016754_697_830_717_830_with_camera_labels.tfrecord,
- •
segment-10963653239323173269_1924_000_1944_000_with_camera_labels.tfrecord,
- •
segment-12161824480686739258_1813_380_1833_380_with_camera_labels.tfrecord,
- •
segment-11928449532664718059_1200_000_1220_000_with_camera_labels.tfrecord,
- •
segment-10444454289801298640_4360_000_4380_000_with_camera_labels.tfrecord.

## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. Why is there no ablation study for **no priors**? This should be as close as possible to the vanilla Video Stable Diffusion.
2. Have you experimented with LiDAR noise / pseudo lidar from MDE methods or maybe a mesh-based method? Otherwise this pipeline is bound to an expensive data acquisition pipeline.
3. Training/inference times are unclear. How can it be that your method is faster than splatting-based methods at 0.9 FPS? The training time should also be discussed but it's not.
4. Could moving objects benefit from a special treatment? I.e, tracking/ inpainting. It doesn't look like the method handles well uncertain areas.
soundness: 3
strengths: ### Novelty
Clever use of pseudo-images obtained through colored point cloud projection as a unified representation for all view priors, simplifying the learning objective for the generative model.
### Evaluation
Introduces two new challenging benchmarks - novel camera synthesis and novel trajectory synthesis.

### Efficiency
The authors claim it takes less computational resources at inference time compared to splatting-based models.

### Performance
Better performance versus contenting methods, especially on poses far away from the original camera poses.
summary: The authors present FreeVS -- a Video Stable Diffusion-based generative view synthesis method for driving scenes that can synthesize high-quality camera views both on and beyond recorded trajectories. 

The key innovation is using pseudo-images created by projecting colored point clouds as a unified representation for view priors. As opposed to recent contenders that rely on gaussian splatting or nerfs to represent the scene, the authors train a diffusion model on colored LiDAR point clouds.

The authors introduce two new benchmarks to evaluate the generated images that are far from the original poses. The method outperforms previous approaches for both traditional and newly proposed benchmarks on the Waymo dataset.
weaknesses: ### Novelty
Engineering work -- it boils down to an addon for Video Stable Diffusion that has colored LiDAR point features concatenated.


### Different type of artfacts
The method trades the gaussian and nerf artifacts with the diffusion ones. While there is no denying that FreeVS works better than the previous attempts from novel views, for single front view, splatting still yields significantly better results (Table 3, front view).

### Evaluation
A single dataset is benchmarked (Waymo Open Dataset).

### Paper quality
Some tables missing numbers -- Table 3, reconstruction time.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: See Weaknesses
soundness: 2
strengths: Impressive results on a challenging task
- Results are clearly better than baselines in novel camera synthesis, multi-view novel frame synthesis, and significantly better in novel trajectory synthesis (FID drops by towards 75%)
- Qualitative comparisons clearly back these results
- Video results show impressive generation well outside input trajectory, while other methods have severe artifacts / fail entirely

Creative use of 3D and off-the-shelf models to enable a non-conventional setup
- Novel View Synthesis is so often limited to input trajectories. In the case of cars, this makes the task fairly straightforward and limited due to constraints on using positions connected to the car.
- Instead, this work approaches prediction several meters away from car trajectories. It does so by utilizing colored LiDAR across multiple views to create point clouds it can project into pseudo-images. This is a nontrivial trick to implement effectively. This work shows it can be useful for training a generative model!
- Evaluation is also tricky in the pseudo-image setting, but FID and 3D Detection mAP are suitable metrics; while of course qualitative results are most important.


***Post-Rebuttal Update*** I leave my score at 6. The reviewers did a good job of addressing my concerns and I feel the paper should be accepted as it offers good contributions in 3D and video generation to yield an effective method. See my response to the rebuttal for more detail.
summary: This paper approaches the task of novel view synthesis of outside-trajectory viewpoints on driving videos. It does so by training a conditional video diffusion model on outside-trajectory views created through projection of existing 3D point clouds. It evaluates generated images outside trajectories using off-the-shelf models. Compared to baselines StreetGaussian, EmerNeRF and 3D-GS, it shows superior results on novel camera synthesis, multi-view novel frame synthesis, and outstanding results on novel trajectory synthesis. Qualitative video results show a significant improvement over baselines.
weaknesses: Could use clearer argument for method leading to performance gain
- Numbers in the ablations table do not match that in comparisons to baselines. Why not?
- Ablations show little impact on performance. When the FID of this method is less than a third of that of baselines, surely more than 10% of performance can be explained by choices. For example, how does training data impact performance? What about pretraining or architecture? If these are important, it feels the architecture should be described in more detail. 
- I infer a lot of the performance is coming from training data, yet the main paper has little information about this. How big is the train set in terms of sequences, if they are chosen from WOD? 
- The alternative explanation I fear is most of the performance is coming from Stable Video Diffusion. It should be very clear how strong this baseline is without the proposed contributions

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: This idea is quite similar to Free View Synthesis which both project source views into target view, you may consider add it as a baseline, at least cite it.

@article{riegler2020free,
  title     = {Free View Synthesis},
  author    = {Gernot Riegler and V. Koltun},
  journal   = {European Conference on Computer Vision},
  year      = {2020},
  doi       = {10.1007/978-3-030-58529-7_37},
  bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/49fae04a4e9383080788759f63dba75c86bd21b0}
}


You may also want to cite magicDrive and MagicDrive3D, as both work on generative driving scenes.
soundness: 3
strengths: 1. Much better (more robust) results over 3D-optimization based approaches (EmerNerf, streetgaussian) on far-away novel veiws, because they typically have overfitting issues.
2. A combination of 3D informtion and 2D diffusion  model that provides both controllability and decent rendering results.
summary: This work focus on novel view synthesis on extrpolated view (i.e. rendered views are far away from source training views).
The authors propose to use reprojected colored lidar points as condition, using a freezed diffusion decoder to achieve high quality synthesized images.
Experiments are conducted on Waymo, demonstrating way better results over other 3d-optimization based approaches (emernerf, streetgaussian)
weaknesses: 1. The rendering speed is very slow,  while 3DGS which can render at real time (50+fps), this hinder the downstream applications that requires realtime efficiency.
2. Inconsistency results because of using large decoder
3. Worse performance compared to 3D-optimization approaches if the novel view are close to source views;

---

confidence: 5
contribution: 3
initial_rating: 6
presentation: 3
questions: See above. I personally like the idea of the paper, but I still have many concerns and would provide a final rating based on the authors' responses.
soundness: 3
strengths: - The paper is well motivated, shows great potential for the real-application of autonomous driving simulation.
- Proposed a "psuedo LiDAR controlnet" for SVD, which is easy yet effective.
- The experimental results and demo video demonstrate the effectiveness of the proposed method.
summary: The paper introduces FreeVS, an approach to view synthesis for driving scenes that overcomes limitations of existing methods, which primarily focus on synthesizing camera views along pre-recorded vehicle trajectories. Traditional methods tend to degrade in performance when synthesizing viewpoints deviate from the recorded trajectory, as these viewpoints lack of groundtruth data. 

To ensure that the generated images remain consistent with the 3D structure of the scene and accurate in terms of viewpoint pose, the authors introduce a pseudo-image representation of view priors based on LiDAR. This representation controls the generation process and allows for the simulation of viewpoint transformations, enabling the model to mimic camera movement in different directions.

The authors proposed two benchmarks for evaluating novel camera synthesis and novel trajectory synthesis. The proposed method is evaluated on Waymo Open Dataset.
weaknesses: - The evaluation is not very comprehensive. The baseline methods are not *specifically* designed for the similar purpose of the paper. 3D-GS is the basic method, EmerNeRF and StreetGaussian are more for the dynamic NeRF/3DGS. There are works that use virtual warping for improving the novel view quality such as [1] [2], that might be better for the baselines.
- The benchmark of novel trajectory synthesis looks interesting to me, however, the authors only show the FID results, while FID is well-known for its instability and unreliability by changing the resolutions, etc. This reminds me of the existing novel trajectory synthesis benchmark [3], the authors should test their methods on such a dataset and demonstrate the *absolute* performance gain using the metrics of PSNR, SSIM, etc.
- I would like to further know the zero-shot generalization of the trained SVD, since the Waymo Open dataset is quite clean for its camera/LiDAR extrinsic calibration, and the number of LiDAR beams is relatively high, it is interesting to show the results of genelizating it to other scenes or datasets, and how the SVD benefits the downstream 3DGS reconstruction.
- What are the results of applying the proposed methods on dynamic scenes?

[1] UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving, ICLR 2024.

[2] HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes, ECCV 2024.

[3] XLD: A Cross-Lane Dataset for Benchmarking Novel Driving View Synthesis, arxiv, 2024.

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: See weakness.
soundness: 3
strengths: The paper is easy to read and the idea is clear.

The method is solid and reasonable, potentially showing good generalizability without training very big models (e.g. video foundation model). Conditioning on pseudo-images can help improve 3D consistency.
summary: This paper addresses the free-view synthesis problem by training a diffusion model conditioned on synthetic images through point cloud projection. During training, it project colored LiDAR points from nearby views into the reference view as a condition image, and finetune a diffusion model so that it can generate the reference view. During inference, condition image can be synthesized for a novel view, which is taken by the diffusion model to generate a realistic image of that view.
weaknesses: 1. the proposed method seems too normal, and I read tons of paper doing similar things -- fine-tune the result based on a synthesized image. The authors also mentioned several such works in the related work. The only difference seems to be that they deal with objects while the authors deal with the driving scene.

2. it seems that the paper did not seriously consider temporal information. There is no way to ensure temporal consistency. Also, it seems that moving objects can cause inconsistency and troubles during pseudo image synthesis while the authors only have one sentence discussing how to address it. I believe it can cause a lot of troubles.

3. There is no discussion about the requirement of the pseudo images. For examples, if the novel view is far from captured views, it surely can cause problems.

Meta-review: This paper presents a video diffusion model conditioned on projected colored LIDAR point clouds, and show that it enables high-fidelity novel view synthesis in autonomous driving scenarios. In particular, compared to prior methods like StreetGaussians which show results from viewpoints limited to driving trajectories, this approach allows rendering from different viewpoints. The other reviewers agree that the empirical results are promising and that the technical approach is sound and well-implemented. The AC agrees with this analysis, and feels this paper would be useful for the community, particularly in spurring view synthesis beyond trajectory viewpoints.

Decision: Accept (Poster)