Pointersect is a plug-and-play rendering algorithm for unseen point clouds without any per-scene optimization. It takes a point cloud (position and optionally color) as input and directly renders images from novel views. The same model also estimates surface normal and depth; it is differentiable and allows scene editing without any retraining.

All results in the webpage are rendered by the same model, trained only on 48 meshes (shown at the bottom).


[1] NPBG++, [link.]

[2] Neural Points, [link.]

[3] Screened Poisson surface reconstruction, [link.]

Related readings and updates.

A Multi-Task Neural Architecture for On-Device Scene Analysis

Scene analysis is an integral core technology that powers many features and experiences in the Apple ecosystem. From visual content search to powerful memories marking special occasions in one’s life, outputs (or "signals") produced by scene analysis are critical to how users interface with the photos on their devices. Deploying dedicated models for each of these individual features is inefficient as many of these models can benefit from sharing resources. We present how we developed Apple Neural Scene Analyzer (ANSA), a unified backbone to build and maintain scene analysis workflows in production. This was an important step towards enabling Apple to be among the first in the industry to deploy fully client-side scene analysis in 2016.

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On-device Panoptic Segmentation for Camera Using Transformers

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