NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes (2308.12967v1)

Abstract: Recent implicit neural representations have shown great results for novel view synthesis. However, existing methods require expensive per-scene optimization from many views hence limiting their application to real-world unbounded urban settings where the objects of interest or backgrounds are observed from very few views. To mitigate this challenge, we introduce a new approach called NeO 360, Neural fields for sparse view synthesis of outdoor scenes. NeO 360 is a generalizable method that reconstructs 360{\deg} scenes from a single or a few posed RGB images. The essence of our approach is in capturing the distribution of complex real-world outdoor 3D scenes and using a hybrid image-conditional triplanar representation that can be queried from any world point. Our representation combines the best of both voxel-based and bird's-eye-view (BEV) representations and is more effective and expressive than each. NeO 360's representation allows us to learn from a large collection of unbounded 3D scenes while offering generalizability to new views and novel scenes from as few as a single image during inference. We demonstrate our approach on the proposed challenging 360{\deg} unbounded dataset, called NeRDS 360, and show that NeO 360 outperforms state-of-the-art generalizable methods for novel view synthesis while also offering editing and composition capabilities. Project page: https://zubair-irshad.github.io/projects/neo360.html

• The paper introduces an image-conditional triplanar representation to synthesize 360° outdoor views from minimal input images.
• It leverages a hybrid voxel and bird’s-eye-view approach with a residual network and MLP decoders to accurately model scene radiance and density.
• Experimental results show a PSNR boost of up to 2.39 over baselines, underscoring its potential for practical novel view synthesis applications.

Analysis of NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes

The paper "NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes" proposes a significant advance in the domain of novel view synthesis, particularly for complex outdoor environments. This work introduces NeO 360, a method that tackles the challenges associated with sparse view synthesis by leveraging a new type of neural representation: an image-conditional triplanar representation. The key innovation lies in its ability to generalize across novel scenes using minimal input data, specifically just one or a few posited RGB images of a scene.

Methodological Insights

NeO 360 differentiates itself from existing methods primarily through its hybrid representation system, which combines voxel-based and bird’s-eye-view (BEV) representations, resulting in a more expressive and computationally efficient model for synthesizing 360-degree views of outdoor scenes. This representation is constructed as a set of orthogonally-placed triplanes that model the 3D environment from various perspectives, merging these into a coherent representation that captures the dynamics of a scene more effectively than conventional approaches.

During inference, the NeO 360 architecture utilizes a residual network backbone to extract features from source images, projects these features into a volumetric grid, and subsequently employs multiple MLP-based decoders to infer the scene's radiance and density fields. The inclusion of both local and global feature pathways enhances the model's capacity to interpolate unseen viewpoints accurately without extensive computational demands typically associated with per-scene optimization in other NeRF approaches.

Experimental Validation

The paper introduces a novel dataset, NeRDS 360, consisting of 75 diverse unbounded scenes. This dataset facilitates a thorough experimental evaluation of NeO 360, demonstrating its superior performance over well-established baselines like NeRF, PixelNeRF, and MVSNeRF. Quantitative results show substantial improvements, with NeO 360 outperforming these methods by a PSNR margin of up to 2.39 in challenging multi-map scenarios. Such results underscore the effectiveness of its image-conditional triplanar approach in handling few-shot novel view synthesis tasks.

Implications and Future Work

The implications of NeO 360 are manifold. Practically, this technique enhances the applicability of novel view synthesis in real-world scenarios such as autonomous vehicle navigation and remote sensing where capturing comprehensive multi-view data is infeasible. Theoretically, the work broadens the understanding of neural field representations, encouraging further exploration into hybrid representations that leverage unique dimensional decompositions to model complex systems.

The proposed model exhibits robust zero-shot performance with potential scalability. Future developments could focus on reducing data annotation requirements, leveraging unsupervised or self-supervised learning paradigms, and extending this methodology to adapt to real-world conditions using transfer learning from simulated organs.

This paper marks a promising step forward in efficiently rendering novel views of highly intricate environments using sparse data, setting the stage for subsequent advances in neural rendering technologies.