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DISTWAR: Fast Differentiable Rendering on Raster-based Rendering Pipelines
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DISTWAR: Fast Differentiable Rendering on Raster-based Rendering Pipelines
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Differentiable rendering is a technique used in an important emerging class of visual computing applications that involves representing a 3D scene as a model that is trained from 2D images using gradient descent. Recent works (e.g. 3D Gaussian Splatting) use a rasterization pipeline to enable rendering high quality photo-realistic imagery at high speeds from these learned 3D models. These methods have been demonstrated to be very promising, providing state-of-art quality for many important tasks. However, training a model to represent a scene is still a time-consuming task even when using powerful GPUs. In this work, we observe that the gradient computation phase during training is a significant bottleneck on GPUs due to the large number of atomic operations that need to be processed. These atomic operations overwhelm atomic units in the L2 partitions causing stalls. To address this challenge, we leverage the observations that during the gradient computation: (1) for most warps, all threads atomically update the same memory locations; and (2) warps generate varying amounts of atomic traffic (since some threads may be inactive). We propose DISTWAR, a software-approach to accelerate atomic operations based on two key ideas: First, we enable warp-level reduction of threads at the SM sub-cores using registers to leverage the locality in intra-warp atomic updates. Second, we distribute the atomic computation between the warp-level reduction at the SM and the L2 atomic units to increase the throughput of atomic computation. Warps with many threads performing atomic updates to the same memory locations are scheduled at the SM, and the rest using L2 atomic units. We implement DISTWAR using existing warp-level primitives. We evaluate DISTWAR on widely used raster-based differentiable rendering workloads. We demonstrate significant speedups of 2.44x on average (up to 5.7x).
Forward citations
Cited by 7 Pith papers
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HiGS: A Hierarchical Rendering Architecture for Real-Time 3D Gaussian Splatting
HiGS achieves up to 15.8x faster real-time 3D Gaussian Splatting by running partitioning at coarse macro-tile scale and rasterization at fine tile scale, issuing work proportional to Gaussians per macro-tile.
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ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction
ContraGS trains 3D Gaussian Splatting directly on codebook-compressed representations, cutting peak model memory ~3.5x with small quality loss.
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TemporalGS: Training-Free Plug-and-Play Acceleration for 3D Gaussian Splatting Rendering via Temporal Priors
TemporalGS accelerates 3DGS rendering up to 1.48× without training by culling redundant Gaussians and selectively rendering only tiles that cannot be warped from temporal geometry and appearance buffers.
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Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication
Splaxel achieves up to 7.6x speedup in distributed 3DGS training on scenes with up to 120M Gaussians by using pixel-level communication and visibility prediction while preserving reconstruction quality.
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DirectFisheye-GS: Enabling Native Fisheye Input in Gaussian Splatting with Cross-View Joint Optimization
Native fisheye projection inside 3DGS plus feature-overlap cross-view joint optimization matches or beats prior fisheye and pinhole Gaussian methods on public datasets.
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Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAM
Pocket-SLAM introduces rendering-area-aware pruning for 3DGS-SLAM, claiming over 60% memory reduction and 2x FPS gain on EuRoC and KITTI while keeping localization and mapping accuracy.
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Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields
Turbo-GS accelerates 3D Gaussian Splatting training via dilated rendering of pixel subsets, convergence-aware Gaussian budget allocation, and combined positional-appearance error densification to enable faster 4K fitt...
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