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elying on a neural network to model the radiance field 3dgs uses 3d gaussians to represent the scene a gaussian in this context is essentially a smooth bell shaped curve that can vary in width height and orientation offering a flexible way to model the density and appearance of different parts of the scene each 3d gaussian models a volumetric splat in the scene with properties like position size orientation and color this representation is both compact and expressive allowing for detailed scenes to be modeled with fewer parameters than a dense voxel grid or a neural network would require to render a new view of the scene the positions and properties of the 3d gaussians are projected onto the 2d image plane of the camera viewpoint this projection translates the 3d gaussians into 2d splats on the image where their contributions are blended together based on their properties and distances to the camera this allows for it to maintain the high visual fidelity and view dependent effects that nerfs are known for but also allows for real time rendering rates and performance including on mobile devices there has been tremendous excitement about another radiance field method 3d gaussian splatting for real time radiance field rendering as its name implies 3dgs is able provide high quality results while still rendering in real time instead of relying on a dense neural network to model the radiance field 3dgs uses 3d gaussians to represent the scene a gaussian in this context is essentially a smooth bell shaped curve that can vary in width height and orientation offering a flexible way to model the density and appearance of different parts of the scene each 3d gaussian models a volumetric splat in the scene with properties like position size orientation and color this representation is both compact and expressive allowing for detailed scenes to be modeled with fewer parameters than a dense voxel grid or a neural network would require the rendering algorithm accounts for the visibility and orientation of each gaussian ensuring that only the visible parts contribute to the final image this includes handling occlusions and leveraging anisotropic directionally varying properties to accurately render surfaces and edges by optimizing the properties of the 3d gaussians directly the method achieves fast training times the renderer designed for efficiency leverages modern gpu architectures to achieve real time rendering speeds while maintaining high visual quality similar to nerfs the input data used are multiple 2d photos from various camera perspectives gaussian splatting is able to create a highly accurate representation of a 3d space both nerfs and 3d gaussian splatting use structure from motion sfm to begin aligning the images but gaussian splatting generates a sparse point cloud additionally because of 3dgs s structure there have also been integrations with some web based platforms such as three js react three fiber and 3d design tool spline these tools help make it possible to bring gaussian splatting into no code web design platforms such as framer and webflow making it easy to show off your creations to render a new view of the scene the positions and properties of the 3d gaussians are projected onto the 2d image plane of the camera viewpoint this projection translates the 3d gaussians into 2d splats on the image where their contributions are blended together based on their properties and distances to the camera the rendering algorithm accounts for the visibility and orientation of each gaussian ensuring that only the visible parts contribute to the final image this includes handling occlusions and leveraging anisotropic directionally varying properties to accurately render surfaces and edges by optimizing the properties of the 3d gaussians directly the method achieves fast training times the renderer designed for efficiency leverages modern gpu architectures to achieve real time rendering speeds while maintaining high visual quality similar to nerfs the input data used are multiple 2d photos from various camera perspectives gaussian splatting is able to create a highly accurate representation of a 3d space both nerfs and 3d gaussian splatting use structure from motion sfm to begin aligning the images but gaussian splatting generates a sparse point cloud who created 3d gaussian splatting 3dgs emerges from the seminal paper 3d gaussian splatting for real time radiance field rendering by bernhard kerbl georgios kopanas thomas leimkühler and george drettakis 3d gaussian splatting or 3dgs has quickly spread across the world and onto a variety of platforms including unreal engine unity nerfstudio polycam luma ai volinga kiri engine and more basically if a company offers a radiance field solution they are also offering an implementation of gaussian splatting at this point 3dgs was released in late april of 2023 and quickly became inordinately popular winning best paper at siggraph 2023 in august gaussian splatting mesh like other radiance field representations such as nerfs meshes are not natively created in the standard reconstruction process however there has been a large volume of research that has gone into pulling meshes from gaussian splatting some existing methods that are gaussian splatting based are 2dgs which uses surfels instead of gaussians rade gs gaussian frosting and texture gs the progress of 3d gaussian splatting gaussian splatting has had a meteoric rise since it was published in early 2023 the pure volume of papers building and exploring in the space has been staggering with large progress being made additionally we have seen several companies building both publicly and in stealth mode begin utilizing 3d gaussian splatting there has also been a surge of using 3d gaussian splatting in both text or image to 3d generative ai models despite them only being created recently its explicit representation makes it easy to work with and thus we have seen people take advantage of it people have also been creating experiential and educational content from gaussian splatting and using the host of viewers and distributors such as spline playcanvas s super splat and antimatter15 s web viewer what s next for gaussian splatting on july 10th 2024 nvidia research unveiled 3d gaussian ray tracing as a departure from splatting as the name implies it utilizes ray tracing rather than the rasterization that has traditionally been used with gaussian splatting there are several exciting advancements and functionalities that become readily available it addresses one of the restrictions of gaussian splatting and allows for the reintroduction of fisheye lenses to captures additionally secondary lighting effects such as refractions shadows depth of field and mirrors this is a brand new line of research and it appears likely that it will attract appropriate research attention to push boundaries forwards with all the advancements in such a short period of time it s crazy to think where 3d gaussian splatting might be headed i expect to see more implementations across various industries that are looking to utilize lifelike 3d content in their offerings on the research side generative ai using gaussians have remained popular and i believe that we will see faster more complex and more hyper realistic outputs recently we have begun to see work to pull high quality meshes out from gaussian splatting outputs such as sugar its follow up paper gaussian frosting and gaustudio there is currently no publicly known ceiling on the technology and will surely be one of the most exciting topics to follow along with over the coming years in its current state 3d gaussian splatting struggles a bit with fine details but another real time radiance field method trilinear point splatting for real time radiance field rendering has proposed a solution additionally on july 10th 2024 nvidia research unveiled 3d gaussian ray tracing as a departure from splatting as the name implies it utilizes ray tracing rather than the rasterization that has traditionally been used with gaussian splatting there are several exciting advancements and functionalities that become readily available it addresses one of the restrictions of gaussian splatting and allows for the reintroduction of fisheye lenses to captures additionally secondary lighting effects such as refractions shadows depth of field and mirrors this is a brand new line of research and it appears likely that it will attract appropriate research attention to push boundaries forwards about neural radiance fields what are neural radiance fields neural radiance fields abbreviated as nerfs are an emerging state of the art solution to the problems of inverse rendering and novel view synthesis in these problems the goal is to take a set of images of a subject from multiple angles and generate the most realistic 3d representation possible using a neural network this allows you to look at the scene or object from any arbitrary angle in addition they also model view dependent lighting effects which means nerfs can capture details like reflections that change depending on your viewing angle our algorithm represents a scene using a fully connected non convolutional deep network whose input is a single continuous 5d coordinate spatial location x y z and viewing direction θ φ and whose output is the volume density and view dependent emitted radiance at that spatial location we synthesize views by querying 5d coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image because volume rendering is naturally differentiable the only input required to optimize our representation is a set of images with known camera poses we describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance and demonstrate results that outperform prior work on neural rendering and view synthesis in other words a deep neural network learns from a sparse set of input images and the network s ability to predict radiance values for every point in the 3d space is honed over time ray casting a technique in which rays are sent from the camera s position into the scene to calculate radiance values plays a crucial role here rendering loss an important metric in this method is optimized to ensure that the rendered outputs closely resemble the training images the continuous nature of the neural radiance field means it doesn t rely on traditional voxel grids or meshes contrasting with older methods of representing scenes despite the complexity and intricacies of how neural radiance fields work the technology s basic steps are straightforward first acquire data usually photographs from different angles using standard cameras or even photogrammetry software next feed this data into deep learning algorithms where the system is trained once trained the nerf can then synthesize or render new views of the scene providing a new perspective previously unimagined who created neural radiance fields introduced by mildenhall et al from berkeley university the nerf representation creates a model in the form of a continuous volumetric scene function this function when queried with a ray casting from a particular viewing direction returns the color and opacity values that when combined generate images that offer new perspectives of static scenes these generated images are remarkably accurate and provide fine details even from viewpoints vastly different from the input views neural radiance fields were originally proposed and the full author list includes matthew tancik ben mildenhall pratul p srinivasan jonathan t barron ravi ramamoorthi and ren ng in 2020 since then a number of breakthroughs have pushed this field of research to the cutting edge one such advancement was instant ngp the project released alongside the research paper by mueller et al instant neural graphics primitives with a multiresolution hash encoding prior to the ingenious introduction of the multi resolution hash encoding it would take hours or even days to produce a high quality nerf now the training process takes less than 15 minutes to produce photorealistic details instant ngp went on to win one of top inventions of 2022 from time magazine the progress of neural radiance fields nerfs are almost something out of science fiction they build upon the technology of light fields using concepts from artificial intelligence machine learning and neural networks they mark a stark divergence from conventional triangle mesh based ray tracing the key difference between nerf and traditional photo scanning is the potential for highly accurate reconstructions that look realistic to the human eye like the name implies nerfs are able to achieve such quality through the clever use of a type of neural network called an mlp multi layer perceptron by training the mlp nerfs are able to approximate the shape and color of reality through a process called differentiable rendering it s amazing but nerfs are also not limited to just one object and can generate novel views of complex scenes and produce amazing results we are still in the early days of nerf and generative ai but the amount of progress made so far has been staggering it seems like every day there s a new nerf paper capable of pushing computer graphics to the next level start ups such as luma ai have greatly reduced the barrier to entry allowing people with just an iphone to capture incredible results now anyone can make a nerf so go grab your digital cameras and start nerfing this publication exists to help highlight the spectacular work that is being done across the world with nerfs and provoke the imagination of readers to embrace what once seemed impossible what s next for nerfs while nerfs offer amazing performance in rendering static scenes their ability to handle dynamic or moving objects in real world scenarios is still an area for future exploration the concept of viewing direction is paramount in nerfs as the representation needs to accurately capture how light from different directions interacts with the scene geometry be it buildings a person or even a simple table the reflection shading and bounce of light off objects and materials are vital to the synthesis process this article just touches on the tip of the iceberg when it comes to understanding nerfs the depth and breadth of this topic are immense and every section from ray casting to deep learning is a deep dive into the exciting world of neural rendering from its beginnings introduced by pioneers like mildenhall et al to its present applications and future potential nerfs stand as a testament to the ever evolving nature of technology importantly as new algorithms and optimization methods are developed we can only look ahead with anticipation at the myriad of possibilities that nerf technology presents radiancefields com documenting the exciting progression of ra...