![]() ![]() Is the unit vector representing view direction. Here one ray point is represented by a 6D tuple ( x, y, z, d x, d y, d z ) where ( x, y, z ) is the 3D location and ( d x, d y, d z ) Since each ray corresponds to a projected pixel in the image plane, N p is also the number of sampled pixels. Formally, the input ray points are represented by a matrix P of shape N p × N r × 6 where N r is the number of ray points corresponding to one ray, and N p is the number of rays sampled in one image. The input to the model is a set of ray points in the 3D space, and the output is a set of pixel colors corresponding to those ray points. Neural renderingĪs shown in Figure 1, we uniformly sample sequences of points on rays in the 3D space, and we call them ray points (they can also be called query points Mildenhall et al. Another important application of NeRF is scene reconstruction Wang et al. ( 2021b) uses a ray transformer to summarize densities from neighborhood views. ( 2020) and Giraffe Niemeyer and Geiger ( 2021) propose generative networks based on NeRF to synthesize new images. ( 2021) reconstructs a non-rigid deformable scene from captured images. Other recent work speeds up training Cheng et al. ( 2020)) use voxel octrees to boost inference in learning scene representations. Neural sparse voxel fields (NSVF Liu et al. ( 2021) structure is adopted to address the ambiguity issue in single views. ( 2021), a transformer architecture for the single-view synthesis task was proposed. We compare the NerFormer thoroughly in the experiments. It replaces the MLP module with a transformer encoder and does not change the ray march module. The NerFormer model is presented along with the CO3D dataset in Reizenstein et al. ( 2020)) provides a simple yet effective solution to the view synthesis problem. The neural radiance field (NeRF Mildenhall et al. We instead address the view synthesis problem in a seq2seq manner where the NeRFA model transfers ray points to pixel colors in the target image. NerFormer replaces the MLP with 3D transformers and builds color and densities from multiple source views. NeRF first predicts the color and densities of the ray points in 3D space, then renders them into a 2D image. Figure 1: Essential difference between NeRF Mildenhall et al. ( 2021) dataset in the category-centric novel view synthesis. Furthermore, NeRFA also surpasses the NeRF Mildenhall et al. ( 2019) datasets for the single-scene view synthesis. ( 2019), and the DeepVoxels Sitzmann et al. The quantitative results also manifest that the NeRFA model consistently outperforms NeRF Mildenhall et al. Visual comparison between NeRF and NeRFA shows that NeRFA produces better fine-grained details of objects. We conduct extensive experiments to validate the effectiveness of the NeRFA model. Single-scene view synthesis and the category-centric novel view synthesis. Over NeRF and NerFormer on four datasets: DeepVoxels, Blender, LLFF, and CO3D.īesides, NeRFA establishes a new state-of-the-art under two settings: the Performs multi-stage attention to reduce the computational overhead.įurthermore, the NeRFA model adopts the ray and pixel transformers to learn the Transformers with the NeRF-like inductive bias. In this way, the feature modulation enhances the The one hand, NeRFA considers the volumetric rendering equation as a softįeature modulation procedure. (NeRF), we propose the NeRF attention (NeRFA) to address the above problems. Second, applying global attention to all raysĪnd pixels is extremely inefficient. Volumetric rendering procedure, and therefore high-frequency components are First, the standard attention cannot successfully fit the Directly applying a standard transformer on this seq2seq formulation Where we take a set of ray points as input and output colors corresponding to In this paper, we present a simple seq2seq formulation for view synthesis ![]()
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