Point-Based Neural Rendering With Neural Point Catacaustics For Interactive Free-Viewpoint Reflection Flow

The visual quality of recent neural rendering techniques is outstanding when used for free-viewpoint rendering of recorded scenes. Such scenes frequently have significant high-frequency view-dependent effects, like reflections from shiny objects, which can be modeled in one of two fundamentally different ways: either using an Eulerian approach, where they take into account a fixed representation of reflections and model directional variation in appearance, or using a Lagrangian solution, where they follow the flow of reflections as the observer moves. By employing either pricey volumetric rendering or mesh-based rendering, most earlier techniques adopt the former by encoding color on fixed points as a function of location and view direction.

Instead, their system uses a Neural Warp Field to directly learn reflection flow as a function of perspective, effectively using a Lagrangian approach. Their point-based neural rendering technique makes their interactive rendering possible, which naturally allows reflection points to be bent by the neural field. Because they frequently combine slow volumetric ray-marching with view-dependent queries to represent (relatively) high-frequency reflections, previous methods sometimes include an inherent compromise between quality and performance. Fast approximation options compromise the clarity and sharpness of the reflection while sacrificing angular resolution. In general, such techniques create reflected geometry behind the reflector by modeling density and view-dependent color parameterized by view direction using a multi-layer perceptron (MLP). When combined with volumetric ray-marching, this frequently produces a “foggy” look, missing precise clarity in reflections.

Even if a recent solution enhances the effectiveness of such techniques, volumetric rendering still needs to be improved. Furthermore, using such techniques makes altering scenes with reflections difficult. The bias towards low frequencies in implicit MLP-based neural radiance fields that they avoid by utilizing a Lagrangian, point-based method endures even when other encodings and parameterizations are used. Their strategy provides two additional benefits: Since there is less cost during inference, interactive rendering is possible, and scene modification is simple thanks to the direct representation. They first extract a point cloud from a multi-view dataset using typical 3D reconstruction techniques after a quick manual step to build a reflector mask on three to four pictures, they optimize two distinct point clouds with additional high-dimensional characteristics.

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The primary point cloud, static throughout rendering, represents the mostly diffuse scene component. In contrast, the second reflection point cloud, whose points are moved by the learned neural warp field, depicts the highly view-dependent reflection effects. During training, the footprint and opacity characteristics carried by points are also tuned for their position. The final picture is created by rasterizing and interpreting the learned characteristics of the two-point clouds using a neural renderer. They are inspired by the theoretical underpinnings of geometric optics of curved reflectors, which demonstrate how reflections from a curved object move over catacaustic surfaces, frequently producing erratic, swiftly moving reflection flows.

They develop a flow field they call Neural Point Catacaustics by training it to learn these trajectories, enabling interactive free-viewpoint neural rendering. Importantly, its point-based representation’s explicitness makes it easier to manipulate scenes containing reflections, such as by modifying reflections or cloning reflecting objects. Before presenting their method, they lay out the geometric foundation of complicated reflection flow for curved reflectors. They then provide the following contributions: 

• A novel direct scene representation for neural rendering that includes a primary point cloud with optimized parameters to represent the remaining scene content and a separate reflection point cloud that is displaced by a reflection neural warp field that learns to compute Neural Point Catacaustics.

• A neural warp field that learns how perspective affects the displacement of reflected spots. Regular training of their end-to-end method, including this field, requires careful parameterization and initialization, progressive movement, and point densification. 

• They also present a general, interactive neural rendering algorithm that achieves high quality for a scene’s diffuse and view-dependent radiance, allowing free-viewpoint navigation in captured scenes and interactive rendering.

They use several captured scenes to illustrate their method and demonstrate its superiority to earlier neural rendering techniques for reflections from curved objects in quantitative and qualitative terms. This method enables quick rendering and manipulation of such scenes, such as editing reflections, cloning reflective objects, or locating reflection correspondences in input images.

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Aneesh Tickoo is a consulting intern at MarktechPost. He is currently pursuing his undergraduate degree in Data Science and Artificial Intelligence from the Indian Institute of Technology(IIT), Bhilai. He spends most of his time working on projects aimed at harnessing the power of machine learning. His research interest is image processing and is passionate about building solutions around it. He loves to connect with people and collaborate on interesting projects.