[Paper] Scaling Point-based Differentiable Rendering for Large-scale Reconstruction

Source: arXiv - 2512.20017v1

Overview

The paper introduces Gaian, a new distributed training system that makes point‑based differentiable rendering (PBDR) practical for high‑resolution, large‑scale 3D reconstructions. By exposing fine‑grained data‑access patterns, Gaian dramatically cuts inter‑GPU communication and boosts training throughput, enabling developers to train PBDR models on massive scenes with commodity GPU clusters.

Key Contributions

• Unified PBDR API – a flexible interface that can host any existing point‑based differentiable renderer without code‑base rewrites.
• Data‑locality aware runtime – automatically analyzes read/write patterns to colocate dependent point clouds and textures, minimizing cross‑node traffic.
• Communication‑reduction techniques – combines selective point sharding, lazy synchronization, and compression to cut network load by up to 91 %.
• Scalable implementation – validated on 4 state‑of‑the‑art PBDR algorithms across 6 datasets, achieving 1.5×–3.7× higher training throughput on up to 128 GPUs.
• Open‑source reference – the authors release Gaian’s core library and example integrations, lowering the barrier for industry adoption.

Methodology

1. Abstraction Layer – Gaian defines a set of primitive operations (e.g., point sampling, attribute aggregation, gradient back‑propagation) that map directly to the mathematical steps of any PBDR pipeline.
2. Static Access Profiling – before training, Gaian runs a lightweight trace to capture which points and texture tiles each GPU reads or writes during a forward‑backward pass.
3. Optimal Sharding – using the profile, Gaian partitions the point cloud into locality groups that are assigned to GPUs so that most accesses stay on‑node.
4. Lazy & Compressed Sync – only the deltas of points that cross shard boundaries are exchanged, and they are quantized/compressed on the fly.
5. Dynamic Rebalancing – if a shard becomes a hotspot (e.g., due to view‑dependent sampling), Gaian can migrate points to balance load without stopping training.

All of this runs on top of standard deep‑learning frameworks (PyTorch/TF) and leverages NCCL for low‑level GPU communication.

Results & Findings

• Communication bottleneck eliminated – the majority of training steps become compute‑bound rather than network‑bound.
• Memory footprint stays constant – Gaian’s sharding does not duplicate point data, allowing larger scenes to fit on the same hardware.
• Algorithm‑agnostic gains – the four integrated PBDR methods (e.g., Neural Point Fields, Differentiable Point Splatting) all saw similar speedups, confirming the generality of the approach.

Practical Implications

• Faster prototype cycles – developers can iterate on new PBDR ideas without waiting hours for a single epoch to finish.
• Cost‑effective scaling – the same reconstruction quality can be achieved on fewer nodes or cheaper cloud instances because network traffic is slashed.
• Real‑time or near‑real‑time pipelines – with reduced latency, Gaian opens the door to on‑the‑fly scene capture for AR/VR, robotics mapping, and digital twin updates.
• Plug‑and‑play integration – existing codebases can adopt Gaian by swapping the renderer’s data loader for the Gaian API, preserving most of the original training logic.

Limitations & Future Work

• Static profiling assumption – Gaian’s initial access pattern analysis may become suboptimal for highly dynamic view trajectories; the authors suggest more frequent re‑profiling.
• Hardware dependence – the current implementation is tuned for NVIDIA GPUs and NCCL; extending to AMD or CPU‑only clusters will require additional engineering.
• Limited support for heterogeneous data – point clouds with per‑point neural networks or complex hierarchical attributes are not yet fully optimized.
• Future directions include adaptive sharding during training, tighter integration with emerging mesh‑based differentiable renderers, and open‑source benchmarks for broader community validation.

Authors

• Hexu Zhao
• Xiaoteng Liu
• Xiwen Min
• Jianhao Huang
• Youming Deng
• Yanfei Li
• Ang Li
• Jinyang Li
• Aurojit Panda

Paper Information

• arXiv ID: 2512.20017v1
• Categories: cs.DC, cs.GR
• Published: December 23, 2025
• PDF: Download PDF