SyntheOcc: Synthesize Geometric-Controlled Street View Images through 3D Semantic MPIs

## Abstract

Abstract The advancement of autonomous driving is increasingly reliant on high-quality annotated datasets, especially in the task of 3D occupancy prediction, where the occupancy labels require dense 3D annotation with significant human effort. In this paper, we propose SytheOcc , which denotes a diffusion model that Synthe size photorealistic and geometric-controlled images by conditioning Occ upancy labels in driving scenarios. This yields an unlimited amount of diverse, annotated, and controllable datasets for applications like training perception models and simulation. SyntheOcc addresses the critical challenge of how to efficiently encode 3D geometric information as conditional input to a 2D diffusion model. Our approach innovatively incorporates 3D semantic multi-plane images (MPIs) to provide comprehensive and spatially aligned 3D scene descriptions for conditioning. As a result, SyntheOcc can generate photorealistic multi-view images and videos that faithfully align with the given geometric labels (semantics in 3D voxel space). Extensive qualitative and quantitative evaluations of SyntheOcc on the nuScenes dataset prove its effectiveness in generating controllable occupancy datasets that serve as an effective data augmentation to perception models.

## 1 Introduction

With the rapid development of generative models, they have shown realistic image synthesis and diverse controllability. This progress has opened up new avenues for dataset generation in autonomous driving [, , , ]. The task of dataset generation is usually modeled as controllable image generation, where the ground truth (e.g. 3D Box) is employed to control the generation of new datasets in downstream tasks (e.g. 3D detection). This approach helps to mitigate the data collection and annotation effort as it can generate labeled data for free. However, a novel task of vital importance, occupancy prediction [, ], poses new challenges for dataset generation compared with 3D detection. It requires finer and more nuanced geometry controllability, which refers to use the occupancy state and semantics of voxels in the whole 3D space to control the image generation. We argue that solving this problem not only allows us to synthesize occupancy datasets, but also empowers valuable applications such as editing geometry to generate rare data for corner case evaluation, as shown in Fig. 1. In the following, we first illustrate why prior work struggles to achieve the above objective, and then demonstrate how we address these challenges.

In the area of diffusion models, several representative works have displayed high-quality image synthesis; however, they are constrained by limited 3D controllability: they are incapable of editing 3D voxels for precise control. For example, BEVGen [] generates street view images by conditioning BEV layouts using diffusion models. MagicDrive [] extend BEVGen and additionally converts the 3D box parameters into text embedding through Fourier mapping that is similar to NeRF [], and uses cross-attention to learn conditional generation. Although these methods achieve satisfactory results in image generation, their 3D controllability is inherently limited. These approaches are restricted to manipulating the scene in types of 3D boxes and BEV layouts, and hardly adapt to finer geometry control such as editing the shape of objects and scenes. Meanwhile, they usually convert conditional input into 1D embedding that aligns with prompt embedding, which is less effective in 3D-aware generation due to lack of spatial alignment with the generated images. This limitation hinders their utility in downstream applications, such as occupancy prediction and editing scene geometry to create long-tailed scenes, where granular volumetric control is paramount in both tasks.

ControlNet [] and GLIGEN [] is another type of prominent method in the field of controllable image generation. These approaches exhibit several desirable attributes in terms of controllability. They leverage conditional images such as semantic masks for control, thereby offering a unified framework to manipulate both foreground and background. However, despite its precise spatial control, ControlNet does not align with our specific requirements. Their conditions of pixel-level images differ fundamentally from what we require in 3D contexts. Our experimental results also find that ControlNet struggles to handle overlapping objects with varying depths (see Fig. 6 (a)), as it only utilizes an ambiguous 2D semantic map as conditional input. As a result, it is non-trivial to extend the ControlNet framework and convey their desirable attributes for 3D conditioning.

To address the above challenges, we propose an innovative representation, 3D semantic multi-plane images (MPIs), which contribute to image generation with finer geometric control. In detail, we employ multi-plane images [] to represent the occupancy, where each plane represents a slice of semantic label at a specific depth. Our 3D semantic MPIs not only preserve accurate and authentic 3D information, but also keep pixel-wise alignment with the generated images. We additionally introduce the MPI encoder to encode features, and the reweighing methods to ease the training with long-tailed cases. As a collection, our framework enables 3D geometry and semantic control for image generation and further facilitates corner case evaluation as depicted in Fig. 1. Finally, experimental results demonstrate that our synthetic data achieve better recognizability, and are effective in improving the perception model on occupancy prediction. In summary, our contributions include:

- •
We present SytheOcc, a novel image generation framework to attain finer and precise 3D geometric control, thereby unlocking a spectrum of applications such as 3D editing, dataset generation, and long-tailed scene generation.
- •
Incorporating the proposed 3D semantic MPI, MPI encoder, and reweighing strategy, we deliver a substantial advancement in image quality and recognizability over prior works.
- •
Our extensive experimental results demonstrate that our synthetic data yields an effective data augmentation in the realm of 3D occupancy prediction.

Figure: Figure 1: A showcase of application of SytheOcc. We enable geometric-controlled generation that conveys the user editing in 3D voxel space to generate realistic street view images. In this case, we create a rare scene that traffic cones block the way. This advancement facilitates the evaluation of autonomous systems, such as the end-to-end planner VAD [], in simulated corner case scenes.
Refer to caption: x1.png

## 2 Related Work

### 2.1 3D Occupancy Prediction

The task of 3D occupancy prediction aims to predict the occupancy status of each voxel in 3D space, as well as its semantic label if occupied. Compared with previous perception methods like 3D object detection, occupancy prediction offers a more detailed and nuanced understanding of the environment, as it provides finer geometric details, is capable of handling general, out-of-vocabulary objects, and finally, enriches the planning stack with comprehensive 3D information. Early methods exploited LiDAR as inputs to complete the 3D occupancy of the entire 3D scene [, ]. Recent methods began to explore the more challenging vision-based 3D occupancy prediction [, , , ]. By predicting the geometric and semantic properties of both dynamic and static elements, 3D occupancy prediction offers a more comprehensive understanding of the surrounding environment.

### 2.2 Diffusion-based Image Generation

Recent advancements in diffusion models (DMs) have achieved remarkable progress in image generation. In particular, Stable Diffusion (SD) [] employs DMs within the latent space of autoencoders, striking a balance between computational efficiency and high image quality. Beyond text control, there is also the introduction of additional control signals. A noteworthy work is ControlNet [], which incorporates a trainable copy of the SD encoder to extract the feature of conditional images and adds it to the UNet feature. It significantly enhances the controllability and unlocking pathways for advanced applications. We refer readers to recent survey [] for more details.

### 2.3 Image Generation in Autonomous Driving

As training neural networks relies heavily on labeled
data, numerous studies are delving into dataset generation to boost training. Lift3D [] designs generative NeRF to synthesize labeled datasets for 3D detection for the first time. Several other works employ BEV layouts to synthesize image data, proving beneficial for perception models. For example, BEVGen [] conditions BEV layouts to generate multi-view street images, while BEVControl [] separately generates foregrounds and backgrounds from BEV layouts. MagicDrive [] generates images with 3D geometry controls by independently encoding objects and maps through a text encoder or map encoder. Compared with MagicDrive, our geometry control is characterized by a more detailed and lossless representation of 3D scenes for control, which poses significant challenges than projected layout or box embedding.

Recently, DriveDreamer [], DrivingDiffusion [], Drive-WM [] and Panacea [] use a ControlNet framework, which involves projecting bounding boxes and road maps onto 2D FoV images as a conditioning input. This approach has proven to be effective for geometric control. However, it is limited in that it only achieves alignment at the 2D-pixel level. Consequently, this method falls short in capturing the depth hierarchy and fails to account for the occlusion relationships present in the 3D real world. Besides, adding a depth channel like Panacea [] may address the limitations of depth order, but it discards the occluded part and only contains partial observation. UrbanGiraffe [] train a generative NeRF to perform image generation. WoVoGen [] creates a 4D world volume feature using occupancy to guide the generation, but seems to rely on object mask guidance.

As described above, most of the prior work is restricted by only modeling a projected primitive of 3D boxes and road maps as conditions. They suffer from ill-posed un-projection ambiguity. In contrast, we model 3D occupancy labels as conditions, as they provide finer geometric details and semantic information. However, designing an input representation of 3D occupancy labels into a 2D diffusion model is challenging. In this paper, we propose a novel representation: 3D semantic Multi-Plane Images (MPIs) as conditional inputs, which not only provide spatial alignment that improves visual consistency, but also encode comprehensive 3D geometric information including occluded parts.

## 3 Method

Figure: Figure 2: The overall architecture of SytheOcc. We achieve 3D geometric control in image generation by utilizing our proposed 3D semantic multiplane images to encode scene occupancy. In our framework, we can edit the occupied state and semantics of every voxel in 3D space to control the image generation, thereby opening up a wide spectrum of applications as shown in the top right.
Refer to caption: x2.png

Overview  The overview of our method is depicted in Fig. 2. Built upon the SD pipeline, we aim to perform geometry-controlled image generation by conditioning on 3D geometry labels with semantics (occupancy labels). One requirement is that the images should faithfully align with the given label. This task is more challenging than conditioned on 3D box due to the sparse and irregular nature of occupancy. We first discuss how to efficiently represent occupancy in Sec. 3.2, followed by our designed MPI encoder to enhance generation quality in Sec. 3.3, and reweighing strategy to handle the long-tailed depth and category in Sec. 3.5.

### 3.1 Representation of Condition: Local Control Aligns Better than Global Control

One of the key challenges is how to represent our conditional occupancy input. A straightforward method [, ] is to convert the 3D occupancy voxel to 1D global embedding that is similar to text embedding, and then use cross-attention to learn controllable generation. However, these global methods can be less effective when dealing with dense or irregular data due to the following reasons: (i) They perform controllable generation through hard encoding the spatial relationship between 1D global embedding and 2D UNet features. (ii) Ignore the underlying geometry alignment between the conditional input and the generated image. In contrast, local methods like ControlNet, directly add spatial features to the UNet features, providing 2D local control with pixel-level spatial alignment. They are better than the global method (see Tab. 1), but suffer from 3D ambiguity (see Fig. 6 (a)). Consequently, this comparison motivates us to seek a more compact and efficient manner to encode and condition our 3D occupancy labels.

### 3.2 Represent Occupancy as 3D Semantic Multiplane Images

It is non-trivial to design a 3D representation for conditioning. To efficiently store both the semantic and geometric information of the irregular occupancy input, we propose to use multiplane images (MPIs) [] as representation. An MPI is composed of a series of fronto-parallel RGBA layers within the frustum of the source camera with a specific viewpoint. These planes are arranged at varying depths, from $d_{min}$ to $d_{max}$, starting from the nearest to the farthest. Each layer of these images contains both an RGB image and an alpha map, which collectively capture the visual and geometric details of the scene at the respective depth. In our work, instead of storing RGB value and alpha map in the original MPI, we store our 3D semantic labels. Each layer of MPI represents the semantic index at the corresponding depth. We display the colored MPI in the top row of Fig. 2 for visual clarity, but we actually use the integer index for learning. We obtain our 3D semantic MPI by:

$$ $\displaystyle P_{l}=(u\times d_{l},~{}v\times d_{l},~{}d_{l})^{T},~{}d_{l}=d_{ min}+(d_{max}-d_{min})\times l/D,$ (1) $\displaystyle\text{{{MPI}}}_{n,l}=\texttt{Interpolate}(\text{{{Occupancy}}},~{ }\mathbf{T_{n}}\cdot\mathbf{K^{-1}_{n}}\cdot P_{l}),$ (2) $\displaystyle\text{{{MPI}}}=\texttt{Concatenate}(\text{{{MPI}}}_{i,j}),~{}i\in (0,N),~{}j\in(0,D),$ (3) $$

where $(u,v)$ is a pixel coordinate in image space, $d_{l}$ is depth value of the $l^{th}$ layer, $n$ denotes the $n^{th}$ camera view. This equation implies we first back project points $P$ in camera frustum space $(u,v,d)$ to Euclid space $(x,y,z)$ by multiplying inverse intrinsic $\mathbf{K^{-1}}$. Then we use transformation matrix $\mathbf{T}$ to map points from camera coordinates to occupancy coordinates. We then use the point coordinates to interpolate the nearest semantic index from the dense occupancy voxel to form a slice of MPI. Finally, we concatenate all slices to form $\text{{{MPI}}}\in\mathbb{R}^{N\times D\times H\times W}$, where $D$ is the number of layers that is set at 256, $N$ is the number of camera views in the case of batch size = 1.

By representing occupancy as 3D semantic MPI, every pixel in MPI contains geometry and semantic information with implicit depth, seamlessly integrating occluded elements, and ensuring a precise spatial alignment with the generated images.

### 3.3 3D Semantic MPI Encoder

To enable local control with spatially aligned conditions, we develop a simple but effective MPI encoder that aligns the 3D multi-plane feature to the latent space of the diffusion model. The purpose of the MPI encoder is to obtain features from multi-plane images to perform 3D-aware image synthesis. Unlike the original ControlNet which downsampling conditional input through 3×3 convolutions with padding, we design a 1×1 convolutional encoder without downsampling to encode features. In detail, the 3D multiplane features which have the sample resolution with latent features, are transformed by a 1×1 convolution layer and ReLU activation [] in the MPI encoder.

After obtaining the multi-scale feature after the MPI encoder, we add the feature to the decoder of diffusion UNet to provide spatial features. Experimental results in Tab. 3 will show that our 1×1 conv in MPI encoder is more effective than 3×3 conv, as the 1×1 conv with receptive field $=1$ provides a spatial align feature to the latent feature in the diffusion UNet. In contrast, 3×3 conv is conducted in a camera frustum space rather than Euclid space, making an imprecise correspondence between 3D multiplane features and 2D image features. Moreover, using 3×3 conv to process 3D semantic MPI will introduce a large computational burden as the channel number increases from 3 channels of RGB to 256 planes. We display our 3D geometry and semantic control property in Fig. 3.

In summary, we chose MPIs as the representation because they (i) Incorporate lossless 3D information, including scene geometry rather than 2.5D depth. (ii) Provide spatially aligned conditional features that naturally extend the ControlNet framework from image level to 3D level. (iii) Capable of representing geometry and semantics including occluded elements.

Figure: Figure 3: Visualizations of geometric controlled generation. Top row: Fusion of 3D semantic MPI. Bottom row: our generation concatenated from neighboring views.
Refer to caption: x3.png

### 3.4 Cross-View and Cross-Frame Attention

The sensor arrangement in a self-driving car usually requires a full surround view of cameras to capture the entire 360-degree environment. To effectively simulate the multi-view and subsequent multi-frame generation, zero-initialized [] cross-view and cross-frame attention are integrated into the diffusion model to maintain consistency between views and frames. Following prior work [, , , ], each cross-view attention allows the target view to access information from its neighboring left and right views, thus training cross-view attention using multi-view consistent images will enforce it to generate the same instance in the overlapping region of multi-view cameras.

$$ $\displaystyle\texttt{Attention}(Q,K,V)=\texttt{softmax}(\begin{matrix}\frac{QK ^{T}}{\sqrt{d}}\end{matrix})\cdot V\text{,}$ (4) $\displaystyle{h}_{out}={h}_{in}+{\textstyle\sum_{i\in\{l,r\}}}\texttt{ Attention}(Q_{in},K_{i},V_{i})\text{,}$ (5) $$

where $l$, and $r$ is the camera view of left and right. $Q_{in}$ and ${h}_{in}$ denotes the query and the hidden state of input view. Similarly, we add cross-frame attention that attend previous frame and future frame to enable video generation. In this case, we use the same formulation while $i\in\{f,h\}$, where $f$ and $h$ is the camera view of future and history frames.

### 3.5 Importance Reweighing

Table: Figure 4: Visualizations of the reweighing function in Eq. 6.

To deal with the extreme imbalance problem between foreground, background, and object categories, and also to ease the training, we propose three types of reweighting methods to improve the generation quality of foreground objects.

Progressive Foreground Enhancement  To mitigate the complexity of the learning task, we propose a progressive reweighting method that incrementally enhances the loss associated with the foreground regions (based on semantic class) as the training progresses. The detailed formulation is:

$$ $w(x,m,n)=\frac{(m-1)}{2}\cdot(1+\cos(\frac{x}{n}\cdot\pi+\pi))+1,$ (6) $$

where $x$ is the current training step, $m$ is the maximum value of weights that set at $2$, and $n$ is the total training steps. This approach is engineered to facilitate a learning trajectory that progresses from simplicity to complexity, thereby aiding in the convergence of the model. This curve can be interpreted as a cosine annealing but inverted to amplify the importance of the foreground region.

Depth-aware Foreground Reweighing  In the meantime, we acknowledge the learning difficulty in different depth places in 3D scenes. Following GeoDiffusion [], we perform depth reweighing to foreground objects by adaptively assigning higher weights to farther foreground areas. This enables the model to focus more thoroughly on hard examples with depth-aware importance reweighting. Instead of using their exponential function to increase weights, we use our designed cosine function Eq. 6 for stability. Here $x$ is the input depth value, and $n$ is the maximum depth that set at $50$.

CBGS Sampling  To deal with the class imbalance problem in driving scenarios, where certain object categories appear infrequently, we employ the Class-Balanced Grouping and Sampling (CBGS) [] to better handle the long-tailed classes. CBGS addresses the challenge of class imbalance by grouping and re-sampling training data to ensure each group has a balanced distribution of sample frequency across different object categories. This method reduces the bias towards more frequent classes and enables better generalization to rare scenarios.

Figure: Figure 5: Visualizations of generated multi-view images. The generation conditions (occupancy labels) are from nuScenes validation set. We highlight that (i) Geometry alignment of trees in red rectangle in (b). (ii) Use text prompt to control high-level appearance in (c,d).
Refer to caption: x5.png

### 3.6 Model Training

To ease the training of the MPI encoder and added attention module, we use a two stage training pipeline. We first train MPI encoder and cross-view attention in a multi-view image generation setting. Then we train cross-frame attention and freeze other components in a video generation setting.

Objective Function  Our final objective function can be formulated as a standard denoising objective with reweighing:

$$ $\mathcal{L}=\mathbb{E}_{\mathcal{E}(x),\epsilon,t}\|\epsilon-\epsilon_{\theta} (z_{t},t,\tau_{\theta}(y))\|^{2}\odot w,$ (7) $$

where $w$ is the multiplication of progressive reweighing and depth-aware reweighing.

## 4 Experiments

### 4.1 Dataset and Setups

We conduct our experiments on the nuScenes dataset [], which is collected using 6 surrounded-view cameras that cover the full 360° field of view around the ego-vehicle. It contains 700 scenes for training and 150 scenes for validation. We resize the original image from 1600 × 900 to 800 × 448 for training. In our work, we use the occupancy label with a resolution of $0.2m$ from OpenOccupancy [] as condition input, while the benchmark of occupancy prediction uses a resolution of $0.4m$ from Occ3D [] dataset for its popularity.

**Table 1: Downstream evaluation on the nuScenes-Occupancy validation set. Based on the used train and val data, two types of settings are reported. The first is to use generated training set to augment the real training set, and evaluate on the real validation set, denoted as Aug. The second is to use pretrained models trained on the real training datasets to test on the generated validation set, denoted as Gen.**
| Method | Train | Val | mIoU | $\blacksquare$ barrier | $\blacksquare$ bicycle | $\blacksquare$ bus | $\blacksquare$ car | $\blacksquare$ cons. veh. | $\blacksquare$ moto. | $\blacksquare$ pedes. | $\blacksquare$ traf. cone | $\blacksquare$ trailer | $\blacksquare$ truck | $\blacksquare$ drive. suf. | $\blacksquare$ other flat | $\blacksquare$ sidewalk | $\blacksquare$ terrain | $\blacksquare$ manmade | $\blacksquare$ vegetation |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Oracle (FB-Occ []) | Real | Real | 39.3 | 45.4 | 28.2 | 44.1 | 49.4 | 25.9 | 28.8 | 28.0 | 27.7 | 32.4 | 37.3 | 80.4 | 42.2 | 49.9 | 55.2 | 42.0 | 37.7 |
| SytheOcc-Aug | Real+Gen | Real | 40.3 | 45.4 | 27.2 | 46.6 | 49.5 | 26.4 | 27.8 | 28.4 | 29.4 | 34.0 | 37.2 | 81.3 | 46.0 | 52.4 | 56.5 | 43.3 | 38.9 |
| MagicDrive | Real | Gen | 13.4 | 0.7 | 0.0 | 11.8 | 32.4 | 0.0 | 6.6 | 2.8 | 0.3 | 2.6 | 19.6 | 60.1 | 12.1 | 26.2 | 23.4 | 15.5 | 12.8 |
| ControlNet | Real | Gen | 17.3 | 17.7 | 0.2 | 13.6 | 21.0 | 0.6 | 0.8 | 8.6 | 10.4 | 6.9 | 11.9 | 67.4 | 18.8 | 36.4 | 36.9 | 20.8 | 22.4 |
| ControlNet+depth | Real | Gen | 17.5 | 19.3 | 0.3 | 14.0 | 23.7 | 1.0 | 0.6 | 9.2 | 9.2 | 5.7 | 12.1 | 68.8 | 19.2 | 36.0 | 35.3 | 19.8 | 22.8 |
| SytheOcc-Gen | Real | Gen | 25.5 | 32.6 | 13.8 | 27.7 | 33.4 | 7.5 | 6.5 | 15.7 | 16.5 | 16.5 | 25.6 | 74.3 | 24.5 | 39.4 | 40.5 | 28.6 | 28.8 |

Networks  We use Stable Diffusion [] v2.1 checkpoint as initialization and only train occupancy encoder, cross-view attention. We additionally add cross-frame attention if in video experiments. We adopt FB-Occ [] as the target model for occupancy prediction for its SOTA performance in this task. The pretrained checkpoint of the network is obtained from their official repository. Since FB-Occ predicts occupancy using only single frame images, we thus train SyntheOcc without cross-frame attention in related experiments. For video generation, we provide experimental results in appendix.

Metrics  We use Frechet Inception Distance (FID) [] to measure the perceptual quality of generated images, and use mIoU to measure the precision of occupancy prediction.

Hyperparameters  We set $D=256$, $d_{min}=0$ and $d_{max}=50$. The depth resolution of MPI is thus higher than occupancy voxel. We train our model in 6 epochs with batch size $=8$. The learning rate is set at $2e^{-5}$. The training phase takes around 1 day using 8 NVIDIA A100 80G GPUs. We use UniPC scheduler  [] with the classifier-free guidance (CFG) [] that is set as 7.0. During inference, we use 20 denoising steps for dataset generation.

Baselines  We compare our method with prior methods in Tab. 1. ControlNet denotes we train a ControlNet using an RGB semantic mask as the condition. ControlNet+depth denotes we add a depth channel after the semantic mask to provide 2.5D depth information. The depth map rendered by occupancy is normalized to [0-255] to accommodate the RGB value. The ControlNet+depth can be regarded as a degradation of SytheOcc which is reduced to a single plane. Then we evaluate MagicDrive since it is the only open-sourced method in this area. MagicDrive separately encodes foreground and background using prompt and BEV layout. Furthermore, we evaluate the image quality (FID []) of our method in Tab. 3. Compared with prior methods, we use a unified 3D representation that seamlessly handles foreground and background, surpassing them by a large margin.

### 4.2 Qualitative Results

High-level Control using Prompt  In Fig. 5 (c,d) and Fig. 6 (c), we demonstrate the capability to employ user-defined prompts to generate images with specific weather conditions and high-level style. Although the nuScenes dataset doesn’t contain rare weather images like snow and sandstorms, our method successfully conveys prior knowledge pretrained from stable diffusion to our scenes. Compared with visualization results in prior work like Fig. 8 of MagicDrive, our method shows better alignment with the text prompt, demonstrating the cross-domain generalization ability of our method.

3D Geometric Control  Our flexible framework enables us to create novel scenes by manipulating voxels as displayed in Fig. 1 and Fig. 3. Basically, we can edit the occupied state and semantics of every voxel in our scenes for generation. We highlight that we can create a hinged-articulated truck and an excavator as shown in Fig. 6 (d,e). The generated excavator image exhibits a remarkable alignment with the input occupancy that is delineated by a black outline.

Figure: Figure 6: Top row: Comparison with ControlNet. We achieve a precise alignment between conditional labels and synthesized images, while ControlNet generates objects with incorrect pose due to ambiguous 2D condition. Mid and Bottom row: Visualizations of geometry-controlled image generation. We can faithfully generate objects with the desired topology in a specific 3D position.
Refer to caption: x6.png

**Table 2: Comparison of FID with previous methods on the nuScenes dataset.**
| Method | Condition Type | FID |
| --- | --- | --- |
| BEVGen [] | BEV map | 25.54 |
| BEVControl [] | BEV map | 24.85 |
| DriveDreamer [] | Box + FoV map | 52.60 |
| MagicDrive [] | Box + BEV map | 16.20 |
| Panacea [] | Box + FoV map | 16.96 |
| Ours | 3D Semantic MPI | 14.75 |

Long-tailed Scene Generation  The flexibility of 3D semantic MPI has conferred significant advantages upon our approach. In the following, we create long-tail scenes that rarely occur in our real world for evaluation. In Fig. 1, we show that we manually add parallel traffic cones in front of the ego vehicle. This scene has never happened in the training dataset, but our geometric controllability provides us the capability to create such data. We then use the created scene to test autonomous driving systems such as end-to-end planner VAD [] to validate its effectiveness. In this case, VAD successfully predicts correct waypoints with the high-level command ‘turn left’. Moreover, in appendix Sec. B, we generate long-tailed scenes with extreme weather such as snow and sandstorms, and evaluate perception model on it to examine its generalizability of rare weather.

Comparison with Baselines
In Fig. 6 (a), we visualize a comparison with ControlNet. We find that ControlNet struggles to distinguish the overlapping instances in 2D-pixel space. This leads to the two parked cars being merged into a single car with incorrect pose. In contrast, our 3D semantic MPIs contain more than 2D semantic mask, but also account for complete scene geometry with occluded parts. Together with our proposed MPI encoder and reweighing strategy, our framework yields a realistic image generation with high-quality label alignment. More comparison is provided in Sec. D.

### 4.3 Quantitative Results

Recognizability, Realism and Controllability Evaluation  To evaluate whether our generated images aligned with given annotations, we provide Gen experiment in Tab. 1. Using the annotation of val set, we synthesize a copy of val set’s images, then use perception model trained on real training set to perform evaluation. The performance will be more effective as it is close to the oracle performance. We find that local method (ControlNet) perform better than global method (MagicDrive). Furthermore, SytheOcc generalizes the locality for 3D conditioning to yield better performance.

Data Augmentation for 3D Occupancy Prediction  Notably, we conduct experiments using our synthesized dataset to enhance the real training set in Tab. 1. We first use the occupancy labels from training set to create a synthetic training set. Then we modify the loading pipeline in perception model to randomly sample images from real dataset or synthetic dataset and train network from scratch. Therefore, our approach preserves the inherent training dynamics of the neural network by solely modifying the training images, without any alteration to the number of training iterations or epochs. As MagicDrive-Aug exhibits numerical overflow when training FB-Occ, which may attributed to unsatisfactory recognizability, we have to omit it and only provide MagicDrive-Gen experiments.

As shown in Tab. 1, where SytheOcc-Aug denotes the augmentation experiments using our generated dataset, shows a satisfactory improvement over the prior state of the art. We emphasize that surpassing the performance of the original dataset is not the primary objective of our work; rather, it is an ancillary benefit that emerges from our framework for geometry-controlled generation.

Ablations  In Tab. 3, we present ablation studies across several design spaces of our model, analogous to the Gen experiment in Tab. 1. We find that our designed MPI encoder of 1×1 conv have significant improvement when compared to the conventional 3×3 conv approach. Besides, our proposed three types of reweighing methods demonstrate a consistent improvement over the baseline. As a result, the improved image quality and label alignment enable higher precision in downstream tasks.

## 5 Limitation and Broader Impacts

Layout Genereation  Our method is restricted in a conditional generation framework that should have a conditional input at first. Our condition signal is from the original dataset annotation. Thus most of the augmented data is generated using the same occupancy layout, or with minimal human editing. Future research can incorporate the recent research [, , , , ] that generates occupancy and traffic descriptions of the scenes to synthesize images with novel occupancy or traffic layouts.

Closed-loop Simulation  Given the underlying diverse and controllable image generation of our method, it would be advantageous and valuable to extend our work to a broader domain such as closed-loop simulation [, ], to enable high-fidelity autonomous systems testing. This line of work can be conducted by utilizing motion conditions to generate future frames as in world model [, , ], or by explicitly modeling scene graph as in the case of UniSim[, ] and NeuroNCAP [].

Long-tailed Scene Generation  In this paper, we only investigate a limited number of long-tailed scene generation and corner case evaluations such as rare layout in Fig. 1 and extreme weather in Sec. B. Future work can extend our framework to (i) Synthesize more samples for tail classes to boost performance. (ii) Generate or replicate large-scale databases of corner cases [] for robust perception.

## 6 Conclusion

In this paper, we propose SytheOcc, an innovative image generation framework that is empowered with geometry-controlled capabilities using occupancy. We introduce a novel 3D representation, 3D semantic MPIs, to address the critical challenge of how to efficiently encode occupancy. This representation not only preserves the authentic and complete 3D geometry details with semantics, but also provides a spatial-align feature representation for 2D diffusion models. With this property, our method enjoys photorealistic appearances and fine-grained 3D controllability, serves as a generative data engine to enable a broad range of applications. Extensive experiments demonstrate that our synthetic data facilitate the training for perception models on occupancy prediction, and provide valuable corner case evaluation in a simulated world.

## Appendix

In the appendix, we provide the following content:

| Sec. A: Statement of Geometric Control. | Sec. E: Results of Video Generation. |
| --- | --- |
| Sec. B: Long-Tailed Scene Evaluation. | Sec. F: Generalize to New Cameras. |
| Sec. C: Ablation of plane number in MPIs. | Sec. G: Impact of Amount of Augment Data. |
| Sec. D: Additional Qualitative Comparison. | Sec. H: Visualization of Failure Cases. |

## Appendix A Statement of Geometric Control

In our paper, we refer the geometric controllable generation as using a voxel grid in 3D space to control the image generation. Although the voxel is a quantized representation of the 3D world, when the resolution goes larger, it can already faithfully represent the geometry detail of scenes. Currently, we are limited by the precision of ground truth labels. The $0.2m$ occupancy grid is a tensor of 500×500×40 that cover a space in x-axis spanning $[-50m,50m]$, y-axis spanning $[-50m,50m]$, z-axis spanning $[-5m,3m]$. In the future, we plan to explore a higher resolution of geometric control to refine our generation.

Except for occupancy, several other 3D representations can be expressed by 3D semantic MPI, such as mesh, dense point clouds, and even 3D boxes or HD maps. The underlying mechanism is to cast several slices of multi-plane images at different depths to retrieve geometric information. Thus, our 3D semantic MPI can be regarded as a general 3D conditioning representation to benefit a wide spectrum of practical systems. These encompass but are not limited to 3D generation such as text2room [], RoomDreamer [], WonderJourney [], and LucidDreamer [], each of which stands to benefit from the rich geometric context provided by our approach.

## Appendix B Long-Tailed Scene Evaluation

In this section, we explore to use SytheOcc to create long-tailed scenes for downstream evaluation. This also stands for evaluating our model using several corner cases. Similar to the SytheOcc-Gen experiment in Tab. 1, we generate a synthetic validation set but use prompts control to manipulate weather patterns or the intensity of illumination.

As depicted in Fig. 7. We create a variety of weather conditions including sandstorms, snow, foggy, rainy, day night, and day time. The motivation behind the creation of these scenes lies in their extreme rarity compared to the ordinary scenes we have captured. The generation of such data is of significant value, as it aids in addressing the long-tailed distribution of scenes, thereby enriching the diversity of our dataset. More visualization is provided in Fig. 13 to Fig. 14.

In Tab. 4, we observe that all kinds of extreme weather lead to a degradation in performance. This observation underscores the limitations of the perception model in terms of its generalizability to infrequent weather scenarios. Among them, we find that foggy, rainy, and day night exert the most severe impact, as they contribute to a large reduction in visibility as shown in Fig. 7. To improve the generalizability to handle various weather conditions, future work can leverage our generated data to cover the long-tailed scenes, or use adversarial search to find severe scenes based on our framework.

**Table 4: Experiments of downstream evaluation on long-tailed scenes with extreme weather.**
| Scenes | Sandstorm | Snow | Foggy | Rainy | Day night | Day time (raw data) |
| --- | --- | --- | --- | --- | --- | --- |
| FB-Occ mIOU | 22.88 | 18.25 | 10.29 | 9.71 | 9.95 | 25.50 |

Furthermore, we perform long-tailed scene evaluation in Fig. 8. We display the failure of the downstream model VAD [] in our synthetic long-tailed scene. In this case, we simulate a foggy environment that the dense fog obscures the majority of the ego view. Our experiment reveals that due to the lack of training images of foggy scenes, VAD erroneously predicts waypoints that would result in a collision with the bus. This experiment elucidates the boundaries and failure cases of the VAD model []. It exposes the limitations of the system under certain conditions, thereby providing insights into scenarios where the model’s performance may be compromised.

Figure: Figure 7: From top to bottom, we display images of fusion of 3D semantic MPI, synthesized images of sandstorm, snow, foggy, rainy, day night, day time, and ground truth.
Refer to caption: x7.jpg

Figure: Figure 8: Use SytheOcc to create long-tailed scenes for testing. Top: In the ordinary scene of a bus placed in front of the ego vehicle, the end-to-end planner VAD [] predicts future waypoints without movement, thus not plotted in the image. Bottom: By harnessing the prompt-level control in our framework, we simulate a scene with the same layout but filled with fog. VAD predicts wrong waypoints that will collide with the bus.
Refer to caption: x8.png

Figure: Figure 9: Comparison with baselines.
Refer to caption: x9.png

## Appendix C Ablation of plane number of MPIs

In our proposed 3D semantic MPIs, the number of planes is a hyperparameter that affects the precision of 3D representation. The plane number can be regarded as the 3D resolution in depth axis. The larger the plane number, the MPI will contain more details. We find that an increase in the number of planes is associated with improved accuracy in downstream tasks. This finding denotes that more condition information leads to better downstream task performance.

**Table 5: Ablation of the number of multi-plane images.**
| Number of Planes | 96 | 128 | 256 |
| --- | --- | --- | --- |
| FB-Occ mIOU | 23.36 | 24.28 | 25.50 |

## Appendix D Qualitative Comparison with Baselines and SOTA

In Fig. 9, we conduct a qualitative comparison of our method against MagicDrive, ControlNet, and ControlNet+depth. We find that all the methods display a satisfactory image quality, as they build upon the foundation of the stable diffusion model. The generation of MagicDrive fails to synthesize barriers as shown in the bottom row. ControlNet struggles to generate objects with the correct pose solely from only 2D conditions as shown in the second row. ControlNet+depth, a degradation of our method, an enhancement over ControlNet in terms of alignment, nevertheless suffers from a loss of finer detail in scenes with heavy occlusion, as shown in the human of the third row. Our method, in contrast, aims to address these challenges and provide a more nuanced and accurate generation of complex scenes.

## Appendix E Extend to Video Generation

As described in the main paper Sec. 3.4, we further extend the cross-view attention to cross-frame attention to perform video generation. Our generation results are Fig. 11, Fig. 12 and Fig. 16. Our implementation is adopted from MagicDrive [] which is similar to Tune-a-video []. The formulation of cross-frame attention is:

$$ $\displaystyle\texttt{Attention}(Q,K,V)=\texttt{softmax}(\begin{matrix}\frac{QK ^{T}}{\sqrt{d}}\end{matrix})\cdot V\text{,}$ (8) $\displaystyle{h}_{out}={h}_{in}+{\textstyle\sum_{i\in\{f,h\}}}\texttt{ Attention}(Q_{in},K_{i},V_{i})\text{,}$ (9) $$

where $f$, and $h$ are the camera view of future and history frames. $Q_{in}$ and ${h}_{in}$ denotes the query and the hidden state of input view. We train our model in a two-stage pipeline. We first train the MPI encoder and cross-view attention in a multi-view image generation setting. Then we train cross-frame attention and freeze other components in a video generation setting.

In practice, we use the keyframe annotation of the nuScenes dataset to train our video model. We start with our pretrained MPI encoder and cross-view attention and only train our cross-frame attention while keeping others frozen. We employ a sequence of 7 frames as a batch, resulting in a batch size of 42 images for the training process.

Given that our primary contribution does not lie in video generation, this experiment serves as a proof of concept, demonstrating the potential of our framework. Future research may extend our methodology to facilitate the generation of longer video sequences, thereby expanding the scope and applicability of our framework.

## Appendix F Generalize to New Cameras

In this section, we investigate the adaptability of our method to a new set of cameras with different intrinsic. Given that our training set has a fixed camera intrinsic and extrinsic, generalizing to novel cameras indicates that our approach possesses robust generalization capabilities. As shown in Fig. 10, benefiting from our local type of condition, SytheOcc generates images that faithfully align with the new intrinsic, proving that SytheOcc do not over-fit certain parameters. Regarding extrinsic parameters, we can cast our MPI at the desirable locations to retrieve geometric information, thus inherently ensuring generalizability without doubt.

Figure: Figure 10: We demonstrate the generalizability of SytheOcc to new camera intrinsic. We multiply factors to the focal length while keeping the resolution the same. In (b,c), focal length $\times 0.8$ denotes a camera with a larger field of view similar to zoom out, focal length $\times 1.2$ denotes a camera with a smaller field of view similar to zoom in.
Refer to caption: x10.png

## Appendix G The Influence of the Amount of Augmented Data

As SytheOcc is capable of generating an infinite number of synthetic data, we investigate the influence of the amount of augmented data on downstream tasks in Tab. 6. We find that when our augmented data is expanded from one-fold to two-fold of the training dataset, the performance of perception model slightly decreases. This may indicate the generated data has an optimal ratio for downstream tasks. Due to limited computational resources, we only experiment with a limited amount of ratio. Future work can conduct more thorough experiments to find a universal theorem.

**Table 6: Ablation of the amount of augmented data.**
| Amount of Augmented Data | 0 (no augmentation) | 1 | 2 |
| --- | --- | --- | --- |
| FB-Occ mIOU | 39.3 | 40.3 | 40.1 |

Figure: Figure 11: Video generation results. In the temporal progression, the distant buildings maintain a high degree of consistency, and objects retain their identical shapes and textures across different views and frames.
Refer to caption: x11.png

Figure: Figure 12: Video generation results of large dynamics scenes. The white car comes across different views and frames depicting consistent shapes with only a slight appearance change.
Refer to caption: x12.jpg

Figure: Figure 13: From top to bottom, we display images of fusion of 3D semantic MPI, synthesized images of sandstorm, snow, foggy, rainy, day night, day time, and ground truth.
Refer to caption: x13.jpg

Figure: Figure 14: Weather variation. Same structure with Fig. 13.
Refer to caption: x14.jpg

Figure: Figure 15: Out of distribution generation. We use prompts to control the high-level appearance of images with specific styles. From top to bottom, we display (1) fusion of 3D semantic MPI. (2) Sunny day. (3) Science fiction style. (4) 8-bit pixel art style. (5) Snowfall. (6) Minecraft style. (7) Pokémon style. (8) Diablo style. (9) Ghibli style. (10) Metropolis style. (11) Gotham style. (12) Ground truth.
Refer to caption: x15.jpg

## Appendix H Failure Cases

We display several failure cases of our method. In Fig. 16, we show a crowd scenes. In this scenario, the excessive number of pedestrians presents a challenge to the cross-view attention and cross-frame attention modules. We find our method incapable of discerning individual entities with clarity. Future research can improve the model capacity or enrich high-quality data to mitigate this problem.

Figure: Figure 16: Failure case of video generation results. Our cross-frame attention module is challenging to distinguish a crowd of people across different views and frames.
Refer to caption: x16.jpg

## Peer Reviews

confidence: 5
contribution: 2
initial_rating: 5
presentation: 2
questions: What specific perception model was used in the experiments?
soundness: 2
strengths: 1. SyntheOcc has potential for generating rare long-tail data that could support downstream tasks in real-world scenarios.
2. The experiments are solid, offering extensive qualitative and quantitative comparisons. The key validation experiments, blending generated data with real data, are particularly convincing.
summary: This paper presents SyntheOcc, a method for generating multi-camera images and videos of driving scenarios, using occupancy and text prompt as guiding inputs. The innovation of SyntheOcc lies in its proposed MPI encoder, which projects the raw occupancy of different depth ranges onto the camera plane, combining them into semantic multiplane images. These semantic multiplane images are then encoded as guidance for image generation. The paper provides a robust qualitative and quantitative comparison of generated images and videos. Additionally, it demonstrates the performance of perception models trained on the synthetic data and tested on real validation sets, as well as perception models trained on real data and tested on synthetic validation sets, to validate the proximity of SyntheOcc-generated images to the real domain.
weaknesses: 1. While the paper proposes the MPIs for encoding occupancy as guidance for image generation, the overall novelty remains unclear in several areas. The core functionalities—such as multi-camera image and video generation for driving scenes—are well-covered in prior works like MagicDrive [1], DriveDreamer [2], and Drive-WM [3], which demonstrate strong temporal consistency in video generation that appears more stable than the qualitative results in this paper. It would be helpful for the authors to clarify the novel aspects of SyntheOcc’s contributions by explicitly identifying any unique advantages or improvements over these methods. Furthermore, key capabilities like generating out-of-domain images and videos via text prompts, using occupancy as guiding inputs, editing by modifying ocupancy elements, and simulating images with varying camera intrinsics are already present in WovoGen [4]. The authors could strengthen the paper by specifying which of these functionalities are advanced by SyntheOcc and justifying their relevance. Providing concrete comparisons to these works, either through experiments or discussion, could more clearly highlight SyntheOcc’s contributions and novelty.
2. The generated results of SyntheOcc show notable issues with road markings (evident in Figures 11, 12, and 13), which could be problematic for certain downstream tasks, such as planning. The paper lacks a systematic evaluation of these synthetic data’s impact on such tasks. Some previous works, like BEVGen [4] and MagicDrive [1], leverage HD maps as guidance, which could effectively resolve such issues. To strengthen the paper, the authors could provide a quantitative analysis comparing the quality of road markings in their generated images to ground truth or to outputs from other methods. Such an analysis would clarify the current limitations and help illustrate areas for potential improvement. Additionally, evaluating the impact of these road marking inconsistencies on specific downstream tasks, such as lane detection or path planning, could further demonstrate the practical implications of this issue and guide refinements to improve SyntheOcc’s usability for these applications.
3. I am not convinced that SyntheOcc effectively expands current datasets. Firstly, the paper mentions (Table 9) that excessive synthetic data can hinder perception model performance. Some corner cases, which require manual adjustment, are the truly valuable data needed in datasets (such as those in the lower parts of Figures 1 and 7). Editing these corner cases still demands considerable manual intervention (e.g., placing barriers on roads, altering road structures, or positioning pedestrians atop vehicles).


references:
[1] Ruiyuan Gao, Kai Chen, Enze Xie, Lanqing Hong, Zhenguo Li, Dit-Yan Yeung, and Qiang Xu. Magicdrive: Street view generation with diverse 3d geometry control. In ICLR, 2024.
[2] Xiaofeng Wang, Zheng Zhu, Guan Huang, Xinze Chen, and Jiwen Lu. Drivedreamer: Towards real-world-driven world models for autonomous driving. In arxiv, 2024.
[3] Yuqi Wang, Jiawei He, Lue Fan, Hongxin Li, Yuntao Chen, and Zhaoxiang Zhang. Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving. In CVPR, 2024.
[4] Jiachen Lu, Ze Huang, Jiahui Zhang, Zeyu Yang, and Li Zhang. Wovogen: World volume-aware diffusion for controllable multi-camera driving scene generation. In ECCV, 2024.
[5] Alexander Swerdlow, Runsheng Xu, and Bolei Zhou. Street-view image generation from a bird’s-eye view layout. IEEE RAL, 2024.

---

confidence: 5
contribution: 2
initial_rating: 5
presentation: 3
questions: Please address the questions raised in the weaknesses section.
soundness: 3
strengths: - [S1: Quality] The paper has demonstrated extensive experimental results and showcased that the proposed method is superior under both real-to-sim evaluation and sim-to-real data augmentation, when compared against existing methods. The paper also includes ablation studies including the MPI encoder architecture and reweighing methods.

- [S2: Clarity] The proposed method is well described in detail with clear illustrations (e.g., Figure 1 and Figure 2).
summary: This paper introduces SyntheOcc, a controllable camera image simulation framework in the autonomous driving domain. The proposed framework uses 3D semantic occupancy grid as the conditions for camera image simulation, where multi-plane semantic images (MPIs) projected from 3D semantic occupancy grids have been used as conditional input to a 2D diffusion model. The effectiveness of SyntheOcc is demonstrated through improved performance in Real-to-sim evaluation and Sim-to-real data augmentation on the NuScenes dataset.
weaknesses: - [W1] The paper does not include several very relevant work in the literature review. Most of the baselines used in the paper come from publications within the past two years. The reviewer feels that two relevant papers on camera simulation is missing [NewRef1] and [NewRef2] from the literature review.

- [W2] This paper does not compare against an important baseline UniSim [Yang et al., CVPR 2023].
While the proposed method is pure data-driven, is it possible to showcase the results on Pandaset used in the Unisim? It is unclear whether the proposed method is superior to UniSim as a camera image simulator or simply works well on Nuscenes dataset but does not generalize to other datasets (e.g., Pandaset, Waymo Open Dataset). The reviewer feels that such discussions and experimental comparisons are needed as a strong justification for acceptance.
  - [W2.1] It is important to understand whether the proposed method is transferrable to other datasets with minimum fine-tuning or adaptation. For example, as shown in Figure 6 of GeoSim paper [NewRef1], the same pipeline works for a different city in the Argoverse dataset.

- [W3] While the data augmentation experimental results are interesting (section 4.3, first two rows in Table 1), this paper does not comment on the role of synthetically generated data in nuScenes occupancy prediction. 
  - [W3.1] The semantic categories with significant improvements are bus, traffic cones, trailer, driving surface, other flat, and sidewalk. It is unclear how and why synthetically generated data help for such categories but not the other categories. Is it possible to provide convincing qualitative examples with explanations? 
  - [W3.2] Comment on the overall improvements to the driving system (e.g., behavior prediction and motion planning). How much does the long-tailed scene generation help the downstream tasks? 

References:
- [NewRef1] GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving, Chen et al., In CVPR 2021.
- [NewRef2] Block-NeRF: Scalable Large Scene Neural View Synthesis, Tancik et al., In CVPR 2022.

---

confidence: 4
contribution: 3
initial_rating: 5
presentation: 3
questions: By conducting these expanded experimental comparisons, the authors can more comprehensively validate their claims and provide more compelling evidence for the effectiveness of the proposed SytheOcc framework.
soundness: 3
strengths: 1. SytheOcc offers finer and precise 3D geometric control, allowing for intricate manipulation of object shapes and scene geometry, which is crucial for tasks like 3D editing and dataset generation.
2. Experimental results demonstrate that the synthetic data generated by SytheOcc exhibit better recognizability, indicating a substantial advancement in image quality over existing methods.
3. The synthetic data produced by SytheOcc prove to be highly effective for data augmentation in 3D occupancy prediction tasks, enhancing the performance and robustness of perception models in such applications.
summary: This paper introduces SytheOcc, a novel image generation framework enabling precise 3D geometric control for applications like 3D editing and dataset generation. By leveraging 3D semantic multiplane images (MPIs), the framework achieves finer geometry and semantic control, enhancing image quality and recognizability. Experimental results show the effectiveness of synthetic data in augmenting 3D occupancy prediction tasks, indicating a significant advancement over existing methods.
weaknesses: 1. The practicality of occupancy editing in 3D space should be addressed. It is crucial to automate or accelerate the editing process to make it feasible for practical applications requiring large amounts of data. The authors may report the time required to generate a new image through editing, and discuss possible solutions to scaling up data.
2. The paper use occupancy as a condition due to its spatial information. It would be beneficial to discuss the fundamental differences between using occupancy and using of depth&segmentation maps as conditions to control image generation [1]. Experimental comparisons between using depth&semantic maps versus occupancy as conditions could be conducted to evaluate metrics like FID and inference time.
3. The multi-view consistency appears not so good. In Figure 5 (b) (c), the color of the car in the first row changes in the second and third images. The authors could includsa comparative analysis of the generation results from different models (e.g., MagicDrive) within the same scene.
4. The concept of imbalance mentioned by the authors in Line 272 requires further clarification. It is essential for the authors to provide a detailed explanation of what this imbalance refers to and how it impacts their proposed framework.
5. The examples of editing provided in paper mostly revolve simple cars. In Figure 6, it would be beneficial to explore if the model can accurately move and position more complex, irregularly shaped vehicles or pedestrians to demonstrate the capability of the framework in generating new, diverse scenes.
6. While the paper discusses some designs related to temporal consistency, only qualitative results are presented. It would be valuable for the authors to report metrics like FVD compared to existing works such as DrivingDiffusion and Panacea to provide a more comprehensive evaluation of the proposed framework.
7. Missing reference: [2-4]

[1] UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild

[2] Generalized Predictive Model for Autonomous Driving

[3] Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability

[4] SimGen: Simulator-conditioned Driving Scene Generation

---

confidence: 5
contribution: 2
initial_rating: 5
presentation: 2
questions: I am still wondering why the paper cannot use ControlNet, as multiplane images are still images and could potentially be used as input for ControlNet.
soundness: 2
strengths: 1. The paper proposes an innovative approach by replacing ControlNet with multiplane images, enhancing image synchronization from occluded views.
2. The provided video effectively demonstrates and supports the proposed method.
3. The writing is clear and easy to follow.
4. The experiments are comprehensive, covering both quantitative and qualitative evaluations.
summary: The paper proposes SyntheOcc, a novel image generation framework that can synthesize photorealistic and geometric-controlled street view images by conditioning on 3D occupancy labels. The key innovation is the use of 3D semantic multi-plane images (MPIs) to efficiently encode 3D geometric information as conditional input to the 2D diffusion model. The extensive experiments demonstrate that the synthetic data generated by SyntheOcc can effectively augment perception models for 3D occupancy prediction tasks.
weaknesses: 1. **Clarification on excluding ControlNet**: The paper should more thoroughly explain the decision to exclude ControlNet. The rationale for why ControlNet fails to meet 3D requirements remains unclear. Since multiplane images could serve as conditions for ControlNet.

2. **Incorporating KPM evaluation for consistency**: It would be beneficial for the paper to include KPM evaluations from Driving into the Future [1] to better assess temporal and multiview consistency.

3. **Additional out-of-domain results**: Presenting more out-of-domain results, such as experiments with variations in camera intrinsic and extrinsic parameters, would explain the ability of generalization of the model.

4. **Weak video quality**: Some objects in the provided video appear twisted or lack realistic representation. And the videos look a little bit unreal but I cannot tell why.

5. **World model integration**: Considering that driving scene generation works nowadays provides world model results—capable of forecasting future layouts and generating future images based on actions. It would be valuable for the paper to explore integration with world models. For example, testing if the generation method can be adapted to synthesize occupancy predictions, as seen in recent work on occupancy-based world models [2]. It will showcase potential for further real-world applications.

[1] Wang, Yuqi, et al. "Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving." CVPR 2024.
[2] Zheng, Wenzhao, et al. "Occworld: Learning a 3d occupancy world model for autonomous driving." ECCV 2024