Neural Point Cloud Rendering via Multi-Plane Projection
Peng Dai* Yinda Zhang* Zhuwen Li* Shuaicheng Liu Bing Zeng
University of Electronic Science and Technology of China
Google Research


Our proposed method synthesize images in novel view by using multi-plane neural point cloud rendering.



We present a new deep point cloud rendering pipeline through multi-plane projections. The input to the network is the raw point cloud of a scene and the output are image or image sequences from a novel view or along a novel camera trajectory. Unlike previous approaches that directly project features from 3D points onto 2D image domain, we propose to project these features into a layered volume of camera frustum. In this way, the visibility of 3D points can be automatically learnt by the network, such that ghosting effects due to false visibility check as well as occlusions caused by noise interferences are both avoided successfully. Next, the 3D feature volume is fed into a 3D CNN to produce multiple planes of images w.r.t. the space division in the depth directions. The multi-plane images are then blended based on learned weights to produce the final rendering results. Experiments show that our network produces more stable renderings compared to previous methods, especially near the object boundaries. Moreover, our pipeline is robust to noisy and relatively sparse point cloud for a variety of challenging scenes.




"Neural Point Cloud Rendering via Multi-Plane Projection",
Peng Dai*, Yinda Zhang*, Zhuwen Li*, Shuaicheng Liu, Bing Zeng
Conference on Computer Vision and Pattern Recongnition (CVPR), 2020

[PDF] [Code] [Slides] [Poster] [supplimentary video]



Recently, neural point cloud rendering has obtained increasing attention. In order to obtain statisfying rendering results, researchers have proposed various techniques, such as neural texture. However, there are weaknesses existing in previous methods, which don't explicitly take point cloud issues into consideration. For example, duo to imprecise depth and estimated camera parameters, point clouds are usually noisy, escipically at objects' boundary. Also, sparse points will result in confusion between foregroud and background. What's more, rasterizing point clouds via z-buffer into the 2D plane tends to cause flickers and non-uniform optimization.



Our method is divided into two parts, the multi-plane based voxelization (left) and multi-plane rendering(right). For the first part, point clouds are re-projected into camera coordinate system to form frustum region and voxelization plus aggregation operations are adopted to generate a multi-plane 3D representation, which will be concatenated with normalized view direction and sent to render network. For the second part, the concatenated input is feed into a 3D neural render network to predict the product with 4 channels (i.e. RGB + blend weight) and the final output is generated by blending all planes. The training process is under the supervision of perceptual loss, and both network parameters and point clouds features are optimized according to the gradient.



Instead of discarding occluded points via z-buffer rasterization, we proposed to keep them in different depth-related planes, Which is proved to effectively aviod artifacts caused by false visibility check.

Due to the sparsity of points, foreground and background informaiton are entangled. Our multi-plane rendering pipeline can appropriately handle it under relatively sparse points.




Last updated: July 2020