ADOP: Approximate Differentiable One-Pixel Point Rendering (2110.06635v3)

Abstract: In this paper we present ADOP, a novel point-based, differentiable neural rendering pipeline. Like other neural renderers, our system takes as input calibrated camera images and a proxy geometry of the scene, in our case a point cloud. To generate a novel view, the point cloud is rasterized with learned feature vectors as colors and a deep neural network fills the remaining holes and shades each output pixel. The rasterizer renders points as one-pixel splats, which makes it very fast and allows us to compute gradients with respect to all relevant input parameters efficiently. Furthermore, our pipeline contains a fully differentiable physically-based photometric camera model, including exposure, white balance, and a camera response function. Following the idea of inverse rendering, we use our renderer to refine its input in order to reduce inconsistencies and optimize the quality of its output. In particular, we can optimize structural parameters like the camera pose, lens distortions, point positions and features, and a neural environment map, but also photometric parameters like camera response function, vignetting, and per-image exposure and white balance. Because our pipeline includes photometric parameters, e.g.~exposure and camera response function, our system can smoothly handle input images with varying exposure and white balance, and generates high-dynamic range output. We show that due to the improved input, we can achieve high render quality, also for difficult input, e.g. with imperfect camera calibrations, inaccurate proxy geometry, or varying exposure. As a result, a simpler and thus faster deep neural network is sufficient for reconstruction. In combination with the fast point rasterization, ADOP achieves real-time rendering rates even for models with well over 100M points. https://github.com/darglein/ADOP

• The paper introduces a novel neural rendering pipeline using one-pixel point splats to accelerate view synthesis with near real-time speed.
• It optimizes both structural and photometric parameters via differentiable rasterization, enhancing camera calibration and image fidelity.
• The framework supports diverse camera models and complex point clouds while reducing computational load through stochastic point discarding.

Approximate Differentiable One-Pixel Point Rendering (ADOP)

The paper "ADOP: Approximate Differentiable One-Pixel Point Rendering" introduces a novel neural rendering pipeline designed to enhance the synthesis of novel views from calibrated images and 3D point cloud reconstructions. This approach leverages differentiable rendering to optimize not only the input parameters but also photometric attributes such as exposure and white balance.

A standout feature of ADOP is its utilization of one-pixel point splats for rasterization. This methodology significantly improves rendering speed compared to existing differentiable rendering techniques that often encounter computational inefficiencies due to complex blending operations. By rendering points as single pixels and utilizing a differentiable rasterizer, ADOP achieves near real-time performance, even on models with over 100 million points. This efficiency is further enhanced by innovative stochastic point discarding which reduces the processing load without compromising image quality.

In the framework, learned neural descriptors are assigned to points in the cloud, and an environment map is employed to provide background scenery. The incorporation of a fully differentiable tone mapping operator aligns the rendered images with realistic camera outputs, handling variable exposure and dynamic range effectively.

The differentiable rasterization technique at the core of ADOP supports optimization of structural parameters, such as camera pose and point positions, through approximate spatial gradient computation. This capability allows the rendering pipeline to inherently refine inaccuracies in the input data, providing robustness against errors from initial camera calibration and point cloud estimations.

The pipeline, by integrating a physically-based photometric camera model, can manage images with varied exposure and white balance, producing high-dynamic-range outputs. As an implication, this quality fosters improvements in the synthesized image fidelity, particularly in handling difficult inputs like those with variable exposure or inaccurate proxy geometry, all while maintaining computational efficiency.

ADOP supports a range of camera models, including fisheye, without requiring preprocessing like undistortion. This adaptability enhances its applicability across a variety of imaging systems and scenarios.

In comparison to existing and emerging methods, such as NPBG, NeRF, and others, ADOP's approach demonstrates superior performance, particularly in terms of computational efficiency and rendered image quality. The pipeline's ability to self-calibrate structural and photometric parameters is noteworthy, offering a practical avenue for high-quality novel view synthesis.

From a theoretical standpoint, the work points towards promising future explorations in extending differentiable rendering to more dynamic environments and varying camera models. The precise correction capabilities of the system suggest potential applications in SLAM, 3D reconstruction, and beyond.

In conclusion, the ADOP framework marks significant advancements in neural rendering, offering an efficient, flexible, and robust solution for high-quality view synthesis with profound implications for both academic research in computer graphics and practical applications in visual media production.