# TriVol: Point Cloud Rendering via Triple Volumes

arXiv: https://arxiv.org/abs/2303.16485 · HTML: https://mlsanity.com/paper/2303.16485/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 50 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 30 of 37)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Convolutional Occupancy Networks](https://mlsanity.com/paper/2003.04618/) (2020)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items](https://mlsanity.com/paper/2204.11918/) (2022)
- [Point-NeRF: Point-based Neural Radiance Fields](https://mlsanity.com/paper/2201.08845/) (2022)
- [StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis](https://mlsanity.com/paper/2110.08985/) (2021)
- [GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images](https://mlsanity.com/paper/2209.11163/) (2022)
- [SynSin: End-to-end View Synthesis from a Single Image](https://mlsanity.com/paper/1912.08804/) (2019)
- [Neural Point-Based Graphics](https://mlsanity.com/paper/1906.08240/) (2019)
- [3D Photography using Context-aware Layered Depth Inpainting](https://mlsanity.com/paper/2004.04727/) (2020)
- [Pushing the Boundaries of View Extrapolation with Multiplane Images](https://mlsanity.com/paper/1905.00413/) (2019)
- [Point-Based Neural Rendering with Per-View Optimization](https://mlsanity.com/paper/2109.02369/) (2021)
- [3D-aware Image Synthesis via Learning Structural and Textural Representations](https://mlsanity.com/paper/2112.10759/) (2021)
- [EpiGRAF: Rethinking training of 3D GANs](https://mlsanity.com/paper/2206.10535/) (2022)
- [Embodied Question Answering in Photorealistic Environments with Point Cloud Perception](https://mlsanity.com/paper/1904.03461/) (2019)
- [Differentiable Point-Based Radiance Fields for Efficient View Synthesis](https://mlsanity.com/paper/2205.14330/) (2022)
- [GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving](https://mlsanity.com/paper/2101.06543/) (2021)
- [Sparse 3D convolutional neural networks](https://mlsanity.com/paper/1505.02890/) (2015)
- [World-Consistent Video-to-Video Synthesis](https://mlsanity.com/paper/2007.08509/) (2020)
- [SMPLpix: Neural Avatars from 3D Human Models](https://mlsanity.com/paper/2008.06872/) (2020)
- [Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations](https://mlsanity.com/paper/2204.08839/) (2022)
- [Neural Point Cloud Rendering via Multi-Plane Projection](https://mlsanity.com/paper/1912.04645/) (2019)

## Built on by (most cited first, 16 of 16)

- [DiffComplete: Diffusion-based Generative 3D Shape Completion](https://mlsanity.com/paper/2306.16329/) (2023)
- [Spectrally Pruned Gaussian Fields with Neural Compensation](https://mlsanity.com/paper/2405.00676/) (2024)
- [Improving Robustness for Joint Optimization of Camera Poses and Decomposed Low-Rank Tensorial Radiance Fields](https://mlsanity.com/paper/2402.13252/) (2024)
- [LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes](https://mlsanity.com/paper/2405.00900/) (2024)
- [PointNeRF++: A multi-scale, point-based Neural Radiance Field](https://mlsanity.com/paper/2312.02362/) (2023)
- [X-Ray: A Sequential 3D Representation For Generation](https://mlsanity.com/paper/2404.14329/) (2024)
- [Delta-Triplane Transformers as Occupancy World Models](https://mlsanity.com/paper/2503.07338/) (2025)
- [HashPoint: Accelerated Point Searching and Sampling for Neural Rendering](https://mlsanity.com/paper/2404.14044/) (2024)
- [Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer](https://mlsanity.com/paper/2411.07899/) (2024)
- [Local Occupancy-Enhanced Object Grasping with Multiple Triplanar Projection](https://mlsanity.com/paper/2407.15771/) (2024)
- [Semantic-Human: Neural Rendering of Humans from Monocular Video with Human Parsing](https://mlsanity.com/paper/2308.09894/) (2023)
- [Real-time Neural Rendering of LiDAR Point Clouds](https://mlsanity.com/paper/2502.11618/) (2025)
- [Sparse Point Cloud Patches Rendering via Splitting 2D Gaussians](https://mlsanity.com/paper/2505.09413/) (2025)
- [GPN: Generative Point-based NeRF](https://mlsanity.com/paper/2404.08312/) (2024)
- [K-Buffers: A Plug-in Method for Enhancing Neural Fields with Multiple Buffers](https://mlsanity.com/paper/2505.19564/) (2025)
- [A Novel Grouping-Based Hybrid Color Correction Algorithm for Color Point Clouds](https://mlsanity.com/paper/2511.02397/) (2025)

## Abstract (arXiv)

Existing learning-based methods for point cloud rendering adopt various 3D representations and feature querying mechanisms to alleviate the sparsity problem of point clouds. However, artifacts still appear in rendered images, due to the challenges in extracting continuous and discriminative 3D features from point clouds. In this paper, we present a dense while lightweight 3D representation, named TriVol, that can be combined with NeRF to render photo-realistic images from point clouds. Our TriVol consists of triple slim volumes, each of which is encoded from the point cloud. TriVol has two advantages. First, it fuses respective fields at different scales and thus extracts local and non-local features for discriminative representation. Second, since the volume size is greatly reduced, our 3D decoder can be efficiently inferred, allowing us to increase the resolution of the 3D space to render more point details. Extensive experiments on different benchmarks with varying kinds of scenes/objects demonstrate our framework's effectiveness compared with current approaches. Moreover, our framework has excellent generalization ability to render a category of scenes/objects without fine-tuning.