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4K4D: Real-Time 4D View Synthesis at 4K Resolution

Zhen Xu, Sida Peng, Haotong Lin, Guangzhao He, Jiaming Sun, Yujun Shen, Hujun Bao, Xiaowei Zhou

arXiv:2310.11448v3cs.CV

TL;DR

Dynamic view synthesis seeks immersive playback from captured videos, but existing methods can be too slow at high resolutions. 4K4D uses a 4D point-cloud representation, hybrid appearance model, and differentiable depth peeling with hardware rasterization. It reports 400 FPS at 1080p and 80 FPS at 4K, with state-of-the-art rendering quality.

  • Problem

    Existing dynamic view-synthesis methods can take seconds or minutes per 1080p image, and their speed remains limited at high resolutions.

    Source: §1 · pp. 1–2

  • Method

    4K4D represents dynamic scenes with a 4D feature-grid-regularized point cloud, a hybrid image-blending and spherical-harmonics appearance model, and differentiable depth peeling that exploits hardware rasterization.

    Source: §1 · p. 2

  • Results

    400 FPS on DNA-Rendering at 1080p and 80 FPS on ENeRF-Outdoor at 4K were reported with an RTX 4090, alongside state-of-the-art rendering quality.

    Source: §1 · p. 2

  • Takeaways & Limitations

    4K4D demonstrates real-time dynamic-scene rendering at 4K resolution while achieving state-of-the-art rendering quality.

    Source: §6 · p. 10

    4K4D does not produce cross-frame point correspondences, and its storage grows linearly with video frames, making long volumetric videos difficult to model.

    Source: §6 · p. 10

Abstract

from arXiv · show

This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K resolution. Recently, some methods on dynamic view synthesis have shown impressive rendering quality. However, their speed is still limited when rendering high-resolution images. To overcome this problem, we propose 4K4D, a 4D point cloud representation that supports hardware rasterization and enables unprecedented rendering speed. Our representation is built on a 4D feature grid so that the points are naturally regularized and can be robustly optimized. In addition, we design a novel hybrid appearance model that significantly boosts the rendering quality while preserving efficiency. Moreover, we develop a differentiable depth peeling algorithm to effectively learn the proposed model from RGB videos. Experiments show that our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an RTX 4090 GPU, which is 30x faster than previous methods and achieves the state-of-the-art rendering quality. Our project page is available at https://zju3dv.github.io/4k4d/.

1. Introduction

Dynamic view synthesis needs high-fidelity, real-time rendering, but existing methods slow sharply at high resolutions. 4K4D combines a regularized 4D point cloud, hybrid appearance model, and hardware rasterization to improve rendering speed while maintaining quality.

1. Introduction

  • Prior neural methods can take seconds or minutes per 1080p image, while MLP Maps falls to 1.3 FPS at 4K despite reaching 41.7 FPS at 384×512.Real-time, high-fidelity rendering is described as important for VR/AR, sports broadcasting, and artistic performance capture.Source: §1 · pp. 1–2
  • 4K4D uses a 4D point cloud and feature grid with differentiable depth peeling that exploits hardware rasterization for fast rendering.The grid assigns features to points, regularizes the point cloud, and supports robust optimization.Source: §1 · p. 2
  • The hybrid appearance model combines image blending with continuous spherical harmonics, using the SH model to compensate for the blending term’s view-direction discreteness.The image-blending network is view-independent and can be precomputed after training; the combined model improves rendering quality by exploiting input-image information.Source: §1 · p. 2
  • 400 FPS on DNA-Rendering at 1080p and 80 FPS on ENeRF-Outdoor at 4K were achieved with an RTX 4090, alongside state-of-the-art rendering quality.The paper reports that 4K4D renders orders of magnitude faster than prior approaches.Source: §1 · p. 2

2. Related Work

Prior dynamic view-synthesis work extends neural representations or accelerates rendering, but high-resolution speed remains a challenge. The overview depicts 4K4D’s point-cloud pipeline and its combination of image blending and spherical harmonics.

2. Related Work

  • Earlier view-synthesis approaches include multi-view images, multi-plane images, light fields, and explicit surfaces or voxels; traditional mesh-sequence methods often require complicated hardware and controlled environments.NeRF [51] represents static-scene radiance fields with coordinate-based MLPs.Source: §2 · p. 2
  • Neural radiance-field methods have been extended to dynamic scenes, but their computational costs can mean seconds or minutes to render moderate-resolution images; image-feature methods also face speed challenges as resolution increases.The passage describes DyNeRF [38] as adding a temporal dimension to NeRF’s 5D input.Source: §2 · p. 2
  • Acceleration methods distill implicit MLPs into voxel grids, explicit surfaces, or point-based representations; 3D Gaussian Splatting [29] uses differentiable splatting but the cited techniques apply only to static scenes.3DGS uses semi-transparent Gaussian ellipsoids with spherical harmonics to render without slow ray marching.Source: §2 · p. 2
  • Figure 2 shows the 4K4D pipeline: space carving initializes point clouds, a 4D feature grid feeds MLPs for geometry and appearance, and differentiable depth peeling learns from multi-view RGB video.Its appearance model combines piece-wise constant image blending with continuous spherical harmonics.Source: Figure 2 · p. 3
  • Dynamic-scene methods accelerate rendering by reducing network evaluations or incorporating correspondences or time-dependency into 3D Gaussian Splatting, but cited approaches remain limited to moderate resolutions or lack results on large, fast motions.The paper contrasts these limits with 4K4D’s real-time 4K rendering and state-of-the-art quality on large-motion data.Source: §2 · p. 3

3. Proposed Approach

4K4D builds a point-cloud-based neural scene representation from coarse point clouds, aiming for robust video-based learning and hardware-accelerated real-time rendering. Its approach combines 4D feature grids, hybrid appearance modeling, differentiable depth peeling, training losses, and inference-time acceleration.

3. Proposed Approach

  • The proposed approach starts from coarse point clouds extracted by space carving and builds a point-cloud neural representation for learning from input videos and hardware-accelerated rendering.The section organizes the method around scene representation, differentiable depth peeling, and optimization on RGB videos.Source: §3 · p. 3

3.1. Modeling Dynamic Scenes with Point Clouds

  • A 4D feature field assigns point features from six feature planes, while point position, radius, and density represent geometry and a hybrid image-blending/SH model represents appearance.The image-blending component is discrete along viewing direction; the SH component supplies continuous view-dependent effects, and its direction-independent blending network can be precomputed.Source: §3.1 · pp. 3–4

3.2. Differentiable Depth Peeling

  • Differentiable depth peeling uses K hardware-rasterized passes to sort points from nearest to farthest at each pixel, then supports volume rendering and differentiable learning from RGB videos.During training, the camera projection is implemented in PyTorch; inference uses hardware rasterization.Source: §3.2 · p. 4

3.3. Training

  • Training compares rendered pixels with ground truth using MSE and perceptual loss, and applies dynamic-region mask supervision to regularize geometry within visual hulls.The perceptual loss compares VGG16 image features; mask supervision uses masks rendered from dynamic-region point clouds.Source: §3.3 · p. 5

3.4. Inference

  • At inference, 4K4D precomputes point properties and streams them asynchronously while rasterizing; half-precision storage and reducing depth-peeling passes each add 20 FPS without visible quality loss.The number of passes is reduced from 15 to 12.Source: §3.4 · p. 5

4. Implementation Details

4K4D is optimized with Adam and initialized using separate procedures for dynamic regions and static backgrounds.

Optimization

  • Models typically converge after 800k iterations for 200-frame sequences, taking around 24 hours on one RTX 4090 GPU.Adam uses a 5e−3 learning rate; point positions use 1e−5, and the LPIPS and mask regularization weights are 1e−3.Source: §4 · p. 5

Initialization of point clouds

  • Dynamic-region point clouds are initialized from segmentation masks and coarse geometry extracted with space carving.Static backgrounds are formed by mask-weighted averaging across frames, then modeled with Instant-NGP to obtain initial point clouds.Source: §4 · p. 5

Optimization

  • Dynamic regions typically contain 250k points per frame, while static backgrounds typically contain 300k points.Differentiable depth peeling uses 15 passes and the nearest 4 input views; reported rendering speed uses an RTX 3090 unless stated otherwise.Source: §4 · p. 5

5. Experiments

Experiments compare 4K4D with prior methods across four dynamic-view-synthesis datasets and report faster rendering with competitive or higher visual quality. Ablations and runtime analyses examine the representation, appearance model, losses, storage, and rendering optimizations.

5. Experiments

  • Experiments cover DNA-Rendering, ENeRF-Outdoor, NHR, and Neural3DV; evaluation uses dynamic regions for DNA-Rendering and NHR, but whole images for ENeRF-Outdoor.The reported experimental resolutions include 1024×1224 and 1125×1536 for DNA-Rendering, and 960×540 for ENeRF-Outdoor.Source: §5 · pp. 5–6

5.1. Comparison Experiments

  • 30x faster than ENeRF [43] with superior rendering quality, and 13x faster than concurrent work on DNA-Rendering [11].The comparison reports results averaged across scenes at 1024×1224 and 1125×1536 resolutions.Source: Table 1 · p. 6, §5.1 · pp. 6–7
  • 4K4D renders above 200 FPS on DNA-Rendering and above 140 FPS on ENeRF-Outdoor, while comparisons report blur or reconstruction failures for competing methods.KPlanes misses detailed appearance and geometry; image-based methods tend to blur occlusions and edges.Source: §5.1 · p. 7, Figure 5 · p. 8

5.2. Ablation Studies

  • Removing the 4D embedding produces blurry, noisy geometry and inferior rendering quality on the 0013 01 sequence.The ablation replaces the embedding with per-frame, per-point optimizable position, radius, density, and scale.Source: §5.2 · p. 8, Table 4 · p. 8
  • Removing cibr reduces appearance detail and geometry quality, while adding an SH coefficient degree changes PSNR only from 30.202 to 30.328.The proposed hybrid appearance model produces higher-fidelity renderings with better details.Source: §5.2 · p. 8
  • Removing Llpips reduces perceptual quality and other metrics, while Lmsk helps regularize dynamic geometry on DNA-Rendering.Source: §5.2 · p. 8
  • Model storage is below 2 MB per frame; encoded source-image videos add 0.419 MB per frame, while cached precomputed data occupies 200 MB per frame in main memory.Precomputing physical properties takes around 2 seconds per frame; point positions account for most model size.Source: §5.2 · pp. 8–9

5.3. Rendering Speed Analysis

  • Precomputation yields a 10x speedup over no cache, the proposed depth peeling is more than 7 times faster than PyTorch3D, and 16-bit floats and 12 passes each add 20FPS.With the interactive GUI included, 4K4D achieves real-time rendering at 4K resolution on the reported hardware.Source: Table 6 · p. 9, §5.3 · p. 10, Table 7 · p. 10

6. Conclusion and Discussion

4K4D combines a 4D-grid-based neural point cloud, hybrid appearance modeling, and differentiable depth peeling for real-time 4K dynamic-scene rendering. The paper reports faster rendering and state-of-the-art quality, while noting constraints on point correspondences and long-video storage.

6. Conclusion and Discussion

  • 4K4D uses a neural point cloud built on a 4D feature grid, a hybrid appearance model, and differentiable depth peeling with hardware rasterization.The paper says the grid naturally regularizes points and that depth peeling supports optimization and efficient rendering.Source: §6 · p. 10
  • More than 30× increase in rendering speed, with over 200FPS at 1080p on an RTX 3090, alongside state-of-the-art rendering quality.The paper identifies missing point correspondences and storage that grows linearly with video frames as limitations.Source: §6 · p. 10
  • Point correspondences across frames are unavailable, and frame-linear storage makes long volumetric videos difficult to model.The paper identifies correspondence modeling and reducing long-video storage as future-work problems.Source: §6 · p. 10

A. Dataset Settings

The appendix reports dataset sequence, view, and frame counts and clarifies source-view use for image-based rendering.

A. Dataset Settings

  • Table A1 lists sequence, view, and frame counts for each dataset used in the experiments.The passage does not provide the counts themselves.Source: A. Dataset Settings · p. 15
  • For image-based baselines and 4K4D, the training view is also the source view for image-based rendering.Source: A. Dataset Settings · p. 15
  • No test views are provided as source views for image-based rendering.Source: A. Dataset Settings · p. 15

B.1. Additional Comparisons on NHR [88]

On NHR, the appendix contrasts 4K4D with prior methods on rendering quality and speed, with metrics averaged over scenes at two image resolutions.

B.1. Additional Comparisons on NHR [88]

  • KPlanes and DyNeRF produce blurry images on fast-motion NHR scenes and render at 2.0 and 0.2 FPS, respectively.Source: §B.1 · p. 15
  • 30FPS vs. 238FPS is the reported NHR frame-rate comparison; 4K4D is described as higher quality and much faster.The NHR images are 512×612 and 384×512, and metrics are averaged over all scenes.Source: §B.1 · p. 15, Table A2 · p. 15
  • ENeRF and MlpMaps render at interactive rates on NHR at moderate resolution, while 4K4D is reported to deliver higher quality and much higher frame rates.Source: §B.1 · p. 15

B.2. Visualization of SH color

The SH color visualization illustrates the model’s fine-level appearance contribution across rotating views.

B.2. Visualization of SH color

  • The SH model supplies fine-level appearance that enables continuous view-dependent effects across five rotating views.The visualization uses sequence 0013 01 of DNA-Rendering.Source: §B.2 · p. 15

B.3. Comparisons with 3DGS [29]

On ENeRF-Outdoor, 4K4D’s hybrid appearance model achieves higher rendering quality than 3DGS, while using far less per-frame storage. 3DGS also renders more slowly and overfits its training view.

B.3. Comparisons with 3DGS [29]

  • 3DGS renders more slowly because its training produced too many points, and it overfits the first frame rather than generalizing to novel views.4K4D is trained for 5,000 iterations, compared with 30,000 for 3DGS under its default settings.Source: §B.3 · p. 16, Figure A3 · p. 17
  • For a 100-frame video, 3DGS storage is 715 MB per frame, versus 4K4D’s 2 MB per frame overhead.The paper attributes 4K4D’s lower storage to its 4D feature grid and IBR model, which use temporal redundancy.Source: §B.3 · p. 16
  • 4K4D’s hybrid image-based appearance is more expressive than 3DGS’s spherical harmonics and achieves higher rendering quality.The comparison is shown in Fig. A3 and Tab. A3.Source: §B.3 · p. 16
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