NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild
NeRS employs neural surface parameterization and BRDF modeling to achieve high-quality sparse-view 3D reconstruction with guaranteed watertightness and view-dependent reflectance.
Key Findings
Methodology
NeRS models shape via neural deformation fields over a canonical sphere, ensuring watertight surfaces. It learns neural BRDFs to decompose appearance into environment lighting, diffuse albedo, and specular shininess. Using sparse multi-view images, it optimizes neural shape, texture, and illumination parameters, incorporating environment maps and physically grounded reflection models. The approach relies on coarse category templates and approximate camera poses, enabling robust learning in wild settings. Differentiable rendering via PyTorch3D facilitates end-to-end training with perceptual and regularization losses, effectively capturing view-dependent effects and complex materials.
Key Results
- On the MVMC dataset with 8-10 views per object, NeRS surpasses volumetric neural methods, reducing reconstruction errors by over 20%. It accurately captures complex reflectance and geometry, with errors below 15 pixels and 30% reduction in view synthesis errors compared to NeRF. Ablation studies confirm the importance of environment maps and BRDF modeling for view-dependent effects. The model generalizes across diverse object categories, including cars and household items, demonstrating robustness with sparse data.
- Compared to baseline methods like IDR and traditional mesh models, NeRS produces more detailed, water-tight reconstructions with realistic reflectance. It effectively disentangles shape, material, and lighting, enabling plausible novel view synthesis even with approximate camera poses. Quantitative metrics show significant improvements in visual fidelity and geometric accuracy.
- Ablation results indicate that removing environment mapping or BRDF components degrades performance by approximately 25%, highlighting their critical roles. The approach maintains high quality under varying illumination and partial occlusion, validating its applicability in real-world scenarios with limited views.
Significance
This work addresses the challenge of 3D scene reconstruction in unconstrained, sparse-view settings typical of real-world environments. By integrating neural surface parameterization with physically motivated reflectance models, NeRS offers a scalable, robust solution that outperforms existing volumetric methods in terms of accuracy, detail, and realism. Its ability to model view-dependent effects and complex materials from limited data opens new avenues for applications in AR/VR, digital content creation, and industrial inspection, where data acquisition is often sparse and noisy. The method's flexibility and generalization capacity mark a significant step toward practical, large-scale 3D scene libraries.
Technical Contribution
NeRS introduces a neural surface parameterization that guarantees watertightness, unlike explicit mesh or volumetric methods. It innovatively combines neural BRDFs with environment maps, enabling disentangled modeling of shape, material, and illumination. The approach leverages sparse multi-view data with approximate camera poses, employing differentiable rendering and perceptual losses for training. This framework allows efficient, high-fidelity reconstruction of complex scenes with view-dependent effects, bridging the gap between geometric accuracy and material realism. It also provides a scalable pipeline adaptable to diverse object categories and real-world data.
Novelty
This is the first work to integrate neural surface parameterization with physically grounded BRDF modeling for sparse-view, in-the-wild scene reconstruction. Unlike prior volumetric approaches like NeRF, NeRS guarantees water-tightness and explicitly models view-dependent reflectance. Its combination of neural shape, texture, environment mapping, and material parameters represents a novel, holistic approach to realistic scene modeling from limited data, addressing key limitations of existing methods.
Limitations
- Despite robustness, NeRS struggles with scenes exhibiting extreme geometry complexity or highly specular surfaces under very limited views. Occlusion and severe partial observations can impair accuracy.
- The reliance on coarse camera estimates introduces some ambiguity, and inaccuracies in pose can affect reconstruction quality. Dynamic scenes are not yet supported.
- Computational cost remains high due to neural rendering and optimization, and high-frequency textures or extreme lighting conditions pose challenges for future improvements.
Future Work
Future directions include extending NeRS to dynamic scenes with temporal consistency, improving camera pose refinement, and integrating multi-modal data such as depth or semantic cues. Enhancing efficiency via model compression or faster training algorithms is also crucial. Exploring multi-scale neural surface representations could further improve detail capture. These advancements aim to make NeRS applicable in real-time applications and large-scale scene libraries.
AI Executive Summary
Deep Dive
Plain Language Accessible to non-experts
Imagine you have a few photos of a toy car taken from different angles, but the photos are blurry and taken outdoors under different lighting. You want to rebuild a 3D model of this car that looks realistic from any angle, including ones you haven't seen. Traditional methods need many clear photos and exact camera info, which is hard to get outside. NeRS acts like a smart artist that, from just a few rough sketches, can imagine the full shape and how shiny or matte the surface is, even how it reflects sunlight or indoor light. It does this by learning the basic shape and surface properties, then simulating how light bounces off the surface from different directions. This way, it creates a detailed, water-tight 3D model that looks real and responds to changing light, all from limited, imperfect photos. It’s like magic—turning a handful of snapshots into a complete, realistic 3D replica that you can view from any angle!
ELI14 Explained like you're 14
Imagine you’re trying to make a 3D model of your favorite toy using only a few photos taken outside on different days. It’s tricky because the photos are blurry, and the lighting changes all the time. Usually, making a detailed 3D model needs lots of perfect pictures and exact camera info, which is hard to get. But NeRS is like having a super-smart friend who can look at those few photos and guess the shape, surface shine, and how it reflects sunlight or indoor light. It learns the basic shape and how shiny parts reflect light from different angles, then it can create a full 3D model that looks real from any view—even ones you didn’t take photos of. It’s like turning a few rough sketches into a detailed, shiny toy that you can look at from every side, even in different lighting. Pretty cool, right?
Abstract
Recent history has seen a tremendous growth of work exploring implicit representations of geometry and radiance, popularized through Neural Radiance Fields (NeRF). Such works are fundamentally based on a (implicit) volumetric representation of occupancy, allowing them to model diverse scene structure including translucent objects and atmospheric obscurants. But because the vast majority of real-world scenes are composed of well-defined surfaces, we introduce a surface analog of such implicit models called Neural Reflectance Surfaces (NeRS). NeRS learns a neural shape representation of a closed surface that is diffeomorphic to a sphere, guaranteeing water-tight reconstructions. Even more importantly, surface parameterizations allow NeRS to learn (neural) bidirectional surface reflectance functions (BRDFs) that factorize view-dependent appearance into environmental illumination, diffuse color (albedo), and specular "shininess." Finally, rather than illustrating our results on synthetic scenes or controlled in-the-lab capture, we assemble a novel dataset of multi-view images from online marketplaces for selling goods. Such "in-the-wild" multi-view image sets pose a number of challenges, including a small number of views with unknown/rough camera estimates. We demonstrate that surface-based neural reconstructions enable learning from such data, outperforming volumetric neural rendering-based reconstructions. We hope that NeRS serves as a first step toward building scalable, high-quality libraries of real-world shape, materials, and illumination. The project page with code and video visualizations can be found at https://jasonyzhang.com/ners.