In this paper, we present a new representation for neural radiance fields that accelerates both the training and the inference processes with VDB, a hierarchical data structure for sparse volumes. VDB takes both the advantages of sparse and dense volumes for compact data representation and efficient data access, being a promising data structure for NeRF data interpolation and ray marching. Our method, Plenoptic VDB (PlenVDB), directly learns the VDB data structure from a set of posed images by means of a novel training strategy and then uses it for real-time rendering. Experimental results demonstrate the effectiveness and the efficiency of our method over previous arts: First, it converges faster in the training process. Second, it delivers a more compact data format for NeRF data presentation. Finally, it renders more efficiently on commodity graphics hardware. Our mobile PlenVDB demo achieves 30+ FPS, 1280×720 resolution on an iPhone12 mobile phone. Check plenvdb.github.io for details.

Augmented Reality (AR) applications aim to provide realistic blending between the real-world and virtual objects. One of the important factors for realistic AR is the correct lighting estimation. In this article, we present a method that estimates the real-world lighting condition from a single image in real time, using information from an optional support plane provided by advanced AR frameworks (e.g., ARCore, ARKit, etc.). By analyzing the visual appearance of the real scene, our algorithm can predict the lighting condition from the input RGB photo. In the first stage, we use a deep neural network to decompose the scene into several components: lighting, normal, and Bidirectional Reflectance Distribution Function (BRDF). Then we introduce differentiable screen-space rendering, a novel approach to providing the supervisory signal for regressing lighting, normal, and BRDF jointly. We recover the most plausible real-world lighting condition using Spherical Harmonics and the main directional lighting. Through a variety of experimental results, we demonstrate that our method can provide improved results than prior works quantitatively and qualitatively, and it can enhance the real-time AR experiences.

In this paper, we present an efficient and robust deep learning solution for novel view synthesis of complex scenes. In our approach, a 3D scene is represented as a light field, i.e., a set of rays, each of which has a corresponding color when reaching the image plane. For efficient novel view rendering, we adopt a two-plane parameterization of the light field, where each ray is characterized by a 4D parameter. We then formulate the light field as a function that indexes rays to corresponding color values. We train a deep fully connected network to optimize this implicit function and memorize the 3D scene. Then, the scene-specific model is used to synthesize novel views. Different from previous light field approaches which require dense view sampling to reliably render novel views, our method can render novel views by sampling rays and querying the color for each ray from the network directly, thus enabling high-quality light field rendering with a sparser set of training images. Per-ray depth can be optionally predicted by the network, thus enabling applications such as auto refocus. Our novel view synthesis results are comparable to the state-of-the-arts, and even superior in some challenging scenes with refraction and reflection. We achieve this while maintaining an interactive frame rate and a small memory footprint.

Given a set of multiple view videos, which records the motion trajectory of an object, we propose to find out the objects' kinematic formulas with neural rendering techniques. For example, if the input multiple view videos record the free fall motion of an object with different initial speed v, the network aims to learn its kinematics: Δ=vt-1over 2 gt2, where Δ, g and t are displacement, gravitational acceleration and time. To achieve this goal, we design a novel framework consisting of a motion network and a differentiable renderer. For the differentiable renderer, we employ Neural Radiance Field (NeRF) since the geometry is implicitly modeled by querying coordinates in the space. The motion network is composed of a series of blending functions and linear weights, enabling us to analytically derive the kinematic formulas after training. The proposed framework is trained end to end and only requires knowledge of cameras' intrinsic and extrinsic parameters. To validate the proposed framework, we design three experiments to demonstrate its effectiveness and extensibility. The first experiment is the video of free fall and the framework can be easily combined with the principle of parsimony, resulting in the correct free fall kinematics. The second experiment is on the large angle pendulum which does not have analytical kinematics. We use the differential equation controlling pendulum dynamics as a physical prior in the framework and demonstrate that the convergence speed becomes much faster. Finally, we study the explosion animation and demonstrate that our framework can well handle such black-box-generated motions.