CrossModalityDiffusion: Multi-Modal Novel View Synthesis with Unified Intermediate Representation

TL;DR

CrossModalityDiffusion is a unified framework that synthesizes multi-modal images from novel viewpoints without prior scene geometry knowledge, leveraging modality-specific encoders and volumetric rendering for consistent scene understanding.

Contribution

It introduces a modular, geometry-aware diffusion-based approach for cross-modality view synthesis without requiring ground truth scene geometry.

Findings

Effective in generating accurate multi-modal views

Ensures consistent geometric understanding across modalities

Validated on synthetic ShapeNet dataset

Abstract

Geospatial imaging leverages data from diverse sensing modalities-such as EO, SAR, and LiDAR, ranging from ground-level drones to satellite views. These heterogeneous inputs offer significant opportunities for scene understanding but present challenges in interpreting geometry accurately, particularly in the absence of precise ground truth data. To address this, we propose CrossModalityDiffusion, a modular framework designed to generate images across different modalities and viewpoints without prior knowledge of scene geometry. CrossModalityDiffusion employs modality-specific encoders that take multiple input images and produce geometry-aware feature volumes that encode scene structure relative to their input camera positions. The space where the feature volumes are placed acts as a common ground for unifying input modalities. These feature volumes are overlapped and rendered into feature images from novel perspectives using volumetric rendering techniques. The rendered feature images are used as conditioning inputs for a modality-specific diffusion model, enabling the synthesis of novel images for the desired output modality. In this paper, we show that jointly training different modules ensures consistent geometric understanding across all modalities within the framework. We validate CrossModalityDiffusion's capabilities on the synthetic ShapeNet cars dataset, demonstrating its effectiveness in generating accurate and consistent novel views across multiple imaging modalities and perspectives.