HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction

## Abstract

Abstract Reconstructing 3D scenes from multiple viewpoints is a fundamental task in stereo vision. Recently, advances in generalizable 3D Gaussian Splatting have enabled high-quality novel view synthesis for unseen scenes from sparse input views by feed-forward predicting per-pixel Gaussian parameters without extra optimization. However, existing methods typically generate single-scale 3D Gaussians, which lack representation of both large-scale structure and texture details, resulting in mislocation and artefacts. In this paper, we propose a novel framework, HiSplat, which introduces a hierarchical manner in generalizable 3D Gaussian Splatting to construct hierarchical 3D Gaussians via a coarse-to-fine strategy. Specifically, HiSplat generates large coarse-grained Gaussians to capture large-scale structures, followed by fine-grained Gaussians to enhance delicate texture details. To promote inter-scale interactions, we propose an Error Aware Module for Gaussian compensation and a Modulating Fusion Module for Gaussian repair. Our method achieves joint optimization of hierarchical representations, allowing for novel view synthesis using only two-view reference images. Comprehensive experiments on various datasets demonstrate that HiSplat significantly enhances reconstruction quality and cross-dataset generalization compared to prior single-scale methods. The corresponding ablation study and analysis of different-scale 3D Gaussians reveal the mechanism behind the effectiveness. Project website: https://open3dvlab.github.io/HiSplat/

## 1 Introduction

As a crucial task in stereo vision, reconstructing 3D scenes from multiple viewpoints has been extensively explored. Recently, 3D Gaussian Splatting Kerbl et al. (); Chen et al. () has emerged, which utilizes 3D Gaussians as explicit scene representation and adopts gradient descent to optimize the corresponding primitives. Different from previous methods based on ray-marching-based volume rendering, e.g., Neural Radiance Fields (NeRF) Mildenhall et al. (), 3D Gaussian splatting (3D-GS) implements an efficient splatting rendering pipeline, enabling faster and higher-quality scene reconstruction without extra depth information. Nevertheless, the original 3D-GS requires a substantial number of multi-view images for per-scene optimization, which hinders its implementation under limited resources and sparse obversed images. To boost its generalization and transferability, data-driven generalizable 3D-GS  Szymanowicz et al. (); Charatan et al. (); Chen et al. (); Zhang et al. (); Wewer et al. () have been proposed. Generalizable 3D-GS leverages networks to feed-forward predict per-pixel Gaussian splatting parameters for unseen scenes, which can be described as mapping each 2D pixel to a fixed number of 3D Gaussians, namely splatter images. By generating splatter images, generalizable 3D-GS allows for high-quality novel view synthesis using only sparse views, especially the most challenging two views, of the new scene without additional optimization during inference.

However, current generalizable 3D-GS methods generate fixed-resolution splatter images using extracted single-scale features. The uniform 3D Gassians result in a lack of hierarchical representation, making it challenging to simultaneously capture large-scale structures and delicate texture details. Consequently, it causes issues such as blurriness, cracks, mislocation, and artefacts as illustrated in Fig. 1. In 2D visual perception tasks such as segmentation and detection, hierarchical and multi-scale representations He et al. (); Liu et al. (); Lin et al. (); Kong et al. (); Ghiasi et al. () play an essential role in effectively capturing high-level semantic information for large-scale objects and structures while preserving low-level details.
Inspired by the success of 2D multi-scale features, a natural question arises: as an explicit 3D representation akin to 2D features, can 3D Gaussians benefit from a hierarchical structure to unleash the potential of representational capabilities? Nonetheless, applying the hierarchical manner to generalizable 3D-GS is not straightforward. A simple preliminary experiment that extracts multi-scale features to generate hierarchical 3D Gaussians and mixes them for rendering has been conducted. As shown in the first line of Table 3, compared with the previous methods, constructing a vanilla hierarchical 3D Gaussians cannot obtain improvement. The core issue lies in that the multi-scale 3D Gaussians are generated independently and suffer from the inability to capture inter-scale information.

Different from predicting multi-scale Gaussians independently, we propose a novel hierarchical generalizable 3D-GS framework, namely HiSplat, for producing 3D Gaussians in a coarse-to-fine manner. Specifically, HiSplat generates large coarse-grained Gaussians to establish the large-scale structure as a skeleton, followed by fine-grained Gaussians around the coarse Gaussians to gradually enhance texture details as decoration. To prompt the interaction of different scales, we propose an attention-based Error Aware Module that allows finer-grained Gaussians to focus on compensating for errors generated by the coarse-grained Gaussians, referred to as Gaussian compensation. Additionally, to facilitate further correction of erroneous Gaussians, we propose a Modulating Fusion Module to reweight the opacities of larger-scale Gaussians based on the rendering quality of input reference images and the current joint features, termed Gaussian repair. By integrating Gaussian Compensation and Repair, the joint optimization of hierarchical 3D Gaussians can be achieved. During training, supervision is applied to the rendering images in every stage to introduce more gradient flow and provide richer depth information, enabling faster convergence and improved localization. During inference, only the final fusion Gaussians are utilized for rendering.

Extensive experiments demonstrate that, compared to previous methods with single-scale 3D Gaussian representations, HiSplat significantly enhances the quality of novel view synthesis, e.g., surpassing the leading open-source method +0.82 PSNR on RealEstate10K, while exhibiting stronger generalization capabilities on diverse unseen scenes, e.g., improving +3.19 PSNR for zero-shot testing on Replica. In summary, our contributions are as follows:

- •
We first study and introduce hierarchical 3D Gassians representation in generalizable 3D-GS. It can simultaneously reconstruct higher-quality large-scale structures and more delicate texture details to significantly alleviate mislocation and artefacts.
- •
We construct a novel generalizable framework named HiSplat, generating hierarchical 3D Gaussians to reconstruct the scene with only two-view reference images. To exploit the inter-scale information and achieve joint optimization, we propose Error Aware Module for Gaussian compensation and Modulating Fusion Module for Gaussian repair.
- •
Comprehensive experiments on various mainstream datasets demonstrate that, compared with previous methods, HiSplat obtains superior reconstruction quality and cross-dataset generalization. Besides, ablation study and analysis on different scale Gaussians explain the effectiveness of HiSplat.

## 2 Related Work

### 2.1 Novel View Synthesis

Novel view synthesis aims to generate photorealistic images from unseen viewpoints given a set of input images. Neural Radiance Fields (NeRF) Mildenhall et al. (); Yu et al. (); Pumarola et al. (); Barron et al. (; ) marked a significant breakthrough by modeling scenes as continuous volumetric radiance fields parameterized with neural networks. Despite achieving high-quality results, NeRF suffers from slow training speeds and high memory usage due to extensive MLP evaluations and the storage of numerous point samples. In contrast, 3D Gaussian Splatting (3D-GS) Kerbl et al. (); Yu et al. (); Yang et al. () offers an explicit scene representation using anisotropic 3D Gaussians, defined by their positions, covariances, colors, and opacities. A differentiable renderer projects these Gaussians onto the image plane, allowing efficient rendering and gradient computation. However, traditional NeRF and 3DGS methods require dense multi-view images for single-scene optimization and lack generalization, performing poorly in sparse-view reconstruction tasks. Our approach addresses these limitations by achieving high generalization with only two-view reference images.

### 2.2 Generalizable 3D Gaussian Splatting

Generalizable 3D Gaussian Splatting has become a key approach for efficient 3D scene representation and novel view synthesis. Splatter Image Szymanowicz et al. () predicts 3D Gaussian parameters from a single image using an image-to-image neural network, enabling ultra-fast single-view 3D reconstruction. PixelSplat Charatan et al. () extends this to sparse multi-view settings, using image pairs and an epipolar transformer to learn cross-view correspondences and predict depth distributions. MVSplat Chen et al. () constructs cost volumes via plane sweeping to capture cross-view similarities, improving geometry reconstruction by predicting depth maps and unprojecting them to obtain Gaussian centers. TranSplat Zhang et al. () employs a transformer-based model for sparse-view reconstruction, using depth-aware deformable matching and monocular depth priors for refinement. Despite these advancements, existing methods often fail to capture fine-grained details due to single-scale features, leading to artefacts and reduced quality, especially in complex regions. To address these limitations, we propose a hierarchical 3D Gaussian splatting method for coarse-to-fine generalizable multi-view reconstruction.

### 2.3 Hierarchical Neural Representation

Hierarchical and multi-scale representations are fundamental in computer vision and graphics Clark (); Burt & Adelson (); Adelson et al. ().
In 2D visual perception, SPP-net He et al. () introduced multi-scale pooling layers for robust recognition across scales. SSD Liu et al. () utilized multi-resolution feature maps for efficient object detection, while FPN Lin et al. () constructed feature pyramids with top-down pathways and lateral connections, enabling high-level semantic features at all scales. Subsequent works Kong et al. (); Ghiasi et al. () continued to refine multi-scale feature representations.
In 3D novel view synthesis, hierarchical approaches have enhanced efficiency and quality. NeRF Mildenhall et al. () used hierarchical sampling with coarse and fine networks. Subsequent works like KiloNeRF Reiser et al. (), and PyNeRF Turki et al. () employed strategies such as spatial partitioning and multi-scale NeRF models to improve rendering speed and quality across varying scene complexities.
Our work extends hierarchical principles to generalizable 3D-GS, drawing inspiration primarily from 2D vision techniques. We are the first to apply hierarchical neural representation to explicit 3D Gaussian representations, unleashing the potential of 3D Gaussian representations in capturing rich scene details across scales.

## 3 Method

### 3.1 Framework Overview

Figure: Figure 2: The overall framework of HiSplat. For simplicity, the situation with two input images is illustrated. HiSplat utilizes a shared U-Net backbone to extract different-scale features. With these features, three processing stages predict pixel-aligned Gaussian parameters with different scales, respectively. Error aware module and modulating fusion module perceive the errors in the early stages and guide the Gaussians in the later stages for compensation and repair. Finally, the fusing hierarchical Gaussians can reconstruct both the large-scale structure and texture details.
Refer to caption: x2.png

The overall framework of HiSplat is illustrated in Figure 2. Given a set of sparse sequential input images with their corresponding and the target camera pose information, HiSplat aims to feed-forward render the photorealistic target images from unseen views. To achieve this, HiSplat adopts a shared U-Net CNN with a multi-view transformer to extract the hierarchical cross-view features. The subsequent processing can be divided into three interrelated stages. For the lowest stage with the lowest resolution, the cross-view features are fed into a CNN with cost volume formulation to predict the depth and Gassian features, which can be decoded into corresponding Gaussian parameters. For the other two higher stages, they utilize the information (depth, features, and image errors) from the lower stages to predict their corresponding Gaussians via the Error Aware Module. Finally, the Modulating Fusion Module is used to adjust the Gaussian parameters and aggregate them to generate fusing multi-scale Gaussians. During training, fusing multi-scale Gaussians in each stage will be used to render target views parallelly with the supervision of ground truth. For inference, only the fusing Gaussians in the last stage are adopted.

### 3.2 Hierarchical Feature Extraction and Depth Estimation

Hierarchical Cross-view Feature Extraction.
To extract hierarchical multi-scale features from input multi-view 2D images, we construct a CNN and Transformer mixed feature extraction backbone network with a U-Net Ronneberger et al. () architecture. Specifically, the network is divided into an encoder and a decoder, each composed of three $3\times 3$ standard residual blocks He et al. (). Each residual block performs upsampling/downsampling to extract features at different scales, and residual connections are used to integrate features of the same scale from the encoder and decoder. At the end of the encoder, features are fed to a multi-view Transformer Xu et al. (; ) that employs cross-attention and self-attention to extract mutual information from multi-view images and injects it into the subsequent decoding stream. For features extracted from different decoder stages, a simple convolutional head is used to obtain the corresponding scale features. To further enhance the generalization of feature extraction (see Table. 3), we follow MVSFormer++ Cao et al. () to introduce features of frozen DINOv2 Oquab et al. () and interpolate DINOv2 features to add with each scale features. Formally, Given $N$ input images $I_{in}^{ref}\in\mathbb{R}^{N\times H\times W\times 3}$ with image size $H\times W$, the cross-view feature for stage $i$ are denoted as

$$ $\displaystyle F_{i}=\mathcal{N}(I_{in}^{ref};\theta_{\mathcal{N}})_{i}+Interp( \mathcal{D}(I_{in}^{ref};\theta_{\mathcal{D}})),$ (1) $$

where $F_{i}\in\mathbb{R}^{N\times\frac{H}{2^{3-i}}\times\frac{W}{2^{3-i}}\times\frac
{C}{2^{i-1}}}$, $C$ is the channel number of $F_{1}$; $\mathcal{N}$ and $\mathcal{D}$ are the U-Net backbone and DINOv2 respectively; $\theta$ is the corresponding parameters of network; $\mathcal{N}(\cdot;\cdot)_{i}$ denotes the output features from $i_{th}$ stage of U-Net backbone decoder; $Interp(\cdot)$ is the interpolating operation.

Depth Estimation. With the camera matrix, depth is used to reproject the image pixel to 3D space, which is important for the correct location of Gaussians. For hierarchical Gaussians, the larger-scale Gaussians form the skeleton, while smaller Gaussians are near larger-scale Gaussians as a supplement. Therefore, the depth prediction principle is estimating the largest-scale Gaussian depth accurately, and using it as a reference to predict the depth of smaller-scale Gaussians. For depth in the first stage, we utilize the cost volume matching in Multi-View Stereo (MVS) Cao et al. (; ); Yao et al. () for accurate depth estimation. Since cost volume matching introduces unbearable computational load at high resolutions, we only apply it in the first stage. Specifically, given the feature $F\in\mathbb{R}^{N\times\frac{H}{4}\times\frac{W}{4}\times C}$ and the corresponding camera pose $P\in\mathbb{R}^{N\times 4\times 4}$, a plane-sweep stereo approach Collins (); Yao et al. (); Im et al. () is utilized to sample $R$ different depth candidates $V=\{d_{1},d_{2},...,d_{R}\}$ from $[d_{near},d_{far}]$. Then, the matching feature $F^{ij}_{warp,k}\in\mathbb{R}^{\frac{H}{4}\times\frac{W}{4}\times C}$ is sampled by warping the $j_{th}$ view feature $F^{j}$ to $i_{th}$ view according to depth plane $d_{k}$, which is denoted as

$$ $\displaystyle F^{ij}_{warp,k}=Warp(F^{j},P^{i},P^{j},d_{k})$ (2) $$

where $Warp$ is the warping and sampling operation Yao et al. (); Xu et al. (); Chen et al. (). The matching features from other views are utilized to generate the cost volume feature $F^{i}_{cv}=\{F_{cv,1}^{i},F_{cv,2}^{i},...,F_{cv,R}^{i}\}$ by matching it with $F^{i}$, computed as

$$ $\displaystyle F_{cv,k}^{i}=\frac{1}{N-1}\sum_{j=1,j\neq i}^{N}\frac{F_{warp,k} ^{ij}\cdot F^{i}}{\sqrt{C}}.$ (3) $$

The cost volume features are fed into a lightweight CNN Chen et al. () to obtain refined $\hat{F}_{cv,k}^{i}$ and the Gaussian feature $F^{GS}$. The depth of $i_{th}$ view $D^{i}\in\mathbb{R_{+}}^{\frac{H}{4}\times\frac{W}{4}}$ is computed as

$$ $\displaystyle D^{i}=softmax(\hat{F}^{i}_{cv})V.$ (4) $$

For Gaussians in subsequent stages, we employ an Error Aware Module to predict their relative depth offsets, enabling the placement of Gaussians in appropriate positions where the large-scale Gaussians lack details or are incorrect.

### 3.3 Error Aware Module

To enable small-scale Gaussians to supplement the lacking details and correct structural errors of the large-scale Gaussians, we render the mixed Gaussians from the previous stage from input views and compute an error map with the input images. A lightweight 2D U-Net Ronneberger et al. (); Zhou et al. () with two predictors is used to aggregate and generate the depth offsets and Gaussian features. To ensure that the decorative Gaussians are always near the skeletal Gaussians, we have restricted the range of depth offsets. Formally, given the images $\tilde{I}_{i-1}^{ref}\in\mathbb{R}^{N\times H\times W\times 3}$ rendered from Gaussians of stage $i-1$, the depth offsets degree $\alpha_{i}\in[0,1]^{N\times\frac{H}{2^{3-i}}\times\frac{W}{2^{3-i}}}$ and Gaussian features $F_{i}^{GS}\in\mathbb{R}^{N\times\frac{H}{2^{3-i}}\times\frac{W}{2^{3-i}}\times
C
_{GS}}$ can be computed as

$$ $\displaystyle(\alpha_{i},F_{i}^{GS})=\mathcal{U}(|\tilde{I}_{i-1}^{ref}-I^{ref }_{in}|,F_{i};\theta_{\mathcal{U}}),$ (5) $$

where $\mathcal{U}$ and $\theta_{\mathcal{U}}$ are the lightweight U-Net and its corresponding weights. Given a maximum depth coefficient $\eta\in[0,1]$ (typical value 0.1), the depth $D_{i}$ in stage $i$ can be computed as

$$ $\displaystyle D_{i}=\Delta D_{i}+Interp(D_{i-1}),\Delta D_{i}=(2\alpha_{i}-1) \cdot\eta Interp(D_{i-1}),$ (6) $$

where $Interp$ is the interpolating operation aiming to upsameple $D_{i-1}$ to the same spacial size as $D_{i}$.
By introducing $\eta$, the $\Delta D_{i}$ can be restricted in $[-\eta Interp(D_{i-1}),\eta Interp(D_{i-1})]$.

### 3.4 Gaussian Parameter Prediction

After obtaining the Gaussian features $F_{i}^{GS}$ and corresponding depth $D_{i}$ in stage $i$, we follow PixelSplat Charatan et al. () to predict the pixel-aligned Gaussian parameters, including the Gaussian center, opacity, covariance, and spherical harmonics coefficients. Specifically, for the Gaussian center, we utilize the $D_{i}$ along the pixel-aligned ray to unproject the 2D pixel to the 3D space location as the Gaussian center. For other Gaussian parameters, stacked convolutional layers with the corresponding predictor are adopted to generate them.

### 3.5 Modulating Fusion Module

Simply fusing Gaussians of different scales struggles with achieving optimal joint optimization, and merely introducing smaller-scale Gaussians cannot fully correct the potential errors generated by large-scale Gaussians. Therefore, inspired by spatial attention Jaderberg et al. (); Almahairi et al. (), we propose the Modulating Fusion Module. It focuses on modulating and reweighting the Gaussians’ opacity in the areas with significant errors. Formally, given the Gaussian features from current and previous stages $F_{i}^{GS}$ and $\{F_{k}^{GS}|k\in[1,i-1]\}$, we obtain the concatenate Gaussians’ features $F^{cat}=\{F^{cat}_{k}|k\in[1,i-1]\}$, denoted as

$$ $\displaystyle F^{cat}_{k}=cat(interp(F_{i}^{GS}),F_{k}^{GS}),$ (7) $$

where $interp(\cdot)$ is interploating $F_{i}^{GS}$ to the same corresponding spatial size as each $F_{k}^{GS}$; $cat(\cdot,\cdot)$ is concatenating in the channel dimension. An $MLP_{1}$ is used to squeeze the dimension of $F^{cat}$ to multiply the corresponding error maps spatially, fellow another $MLP_{2}$ with sigmoid outputs the modulating coefficient $\xi_{k}\in[0,1]^{N\times\frac{H}{2^{3-k}}\times\frac{W}{2^{3-k}}}$ of previous Gaussians’ opacity $O_{k}\in[0,1]^{N\times\frac{H}{2^{3-k}}\times\frac{W}{2^{3-k}}}$, denoted as

$$ $\displaystyle\xi_{k}=Sigmoid(MLP_{2}(MLP_{1}(F^{cat}_{k};\theta_{MLP_{1}}) \cdot interp(|\tilde{I}_{k}^{ref}-I^{ref}_{in}|);\theta_{MLP_{2}})),$ (8) $$

where $Sigmoid(\cdot)$ is the non-linear sigmoid operation; $\theta_{MLP}$ is the corresponding weights. The previous Gaussians’ opacity $\{O_{1},O_{2},...,O_{i-1}\}$ is updated following $O_{k}:=O_{k}\cdot\xi_{k}$.

### 3.6 Training objective

During training, we render the fusing hierarchical Gaussians in all stages to obtain images from target novel views and utilize the photometric losses Charatan et al. (); Chen et al. (), including Mean Squared Error (MSE) loss and Learned Perceptual Image Patch Similarity (LPIPS) losses Zhang et al. (), to supervise each image. The all training objective is

$$ $\displaystyle\mathcal{L}_{all}=\sum_{i=1}^{3}\lambda_{mse}\mathcal{L}_{mse}( \tilde{I}_{i}^{tar},I^{tar})+\lambda_{lpips}\mathcal{L}_{lpips}(\tilde{I}_{i}^ {tar},I^{tar}),$ (9) $$

where the loss coefficient $\lambda_{mse}=1$, $\lambda_{lpips}=0.05$.

## 4 Experiment

### 4.1 Experimental Setting

Datasets. To comprehensively evaluate the reconstruction ability, we train and test models in two large-scale datasets, RealEstate10K Zhou et al. () and ACID Liu et al. (). The RealEstate10K dataset comprises videos sourced from YouTube, divided into 67,477 training scenes and 7,289 testing scenes. The ACID dataset consists of nature scenes captured via aerial drones, with 11,075 scenes for training and 1,972 scenes for testing. Both datasets are calibrated with Structure-from-Motion (SfM) Schonberger & Frahm () algorithm to estimate camera intrinsic and extrinsic parameters for each frame. Following the novel view synthesis settings of previous works Charatan et al. (); Chen et al. (); Zhang et al. (), two context images are as input, and three novel target views are rendered for each test scene. Besides, to compare the cross-dataset generalization ability, we select other two multi-view datasets, including real object-centric dataset DTU Jensen et al. () and synthetic indoor dataset Replica Straub et al. (), for zero-shot test (without fine-tuning or training). Following  Chen et al. () and  Zhi et al. (), we select sixteen scenes in DTU and eight scenes in Replica for testing. To quantitatively measure the rendering quality of novel views from different aspects, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM) Wang et al. () and Learned Perceptual Image Patch Similarity (LPIPS) losses Zhang et al. () are adopted as testing metrics.

Implementation Details.
For a fair comparison, we follow the commonly used training settings Chen et al. (); Charatan et al. (); Zhang et al. (). Specifically, the input images are resized as $256\times 256$, and the model is optimized by Adam Kingma () for 300,000 iterations. The training experiments are implemented in 8 RTX4090 with batch size 2 for two days. There are more implementation details in A.1.

### 4.2 Main Results

#### 4.2.1 Novel views synthesis

To verify the effectiveness of the proposed HiSplat on novel view synthesis, we train and test the model on large-scale multi-view datasets RealEstate10K and ACID, respectively. We select four typical feed-forward NeRF-based methods, including pixelNeRF Yu et al. (), GPNR Suhail et al. (), AttnRend Du et al. (), MuRF Xu et al. (), and three recent Gaussian-Splatting-based methods, including PixelSplat Charatan et al. (), MVSpalt Chen et al. (), TranSplat Zhang et al. () as comparisons. As shown in Table 1, compared with previous methods, HiSplat can consistently achieve state-of-the-art (SOTA) performance on different datasets and metrics with significant improvement, which demonstrates the superiority of the proposed hierarchical structure of Gaussian primitives. Specifically, on RealEstate10K dataset, HiSplat surpasses the latest state-of-the-art (SOTA) open-source method MVSplat by +0.82 PSNR, and exceeds the leading method Transplat by +0.52 PSNR, achieving a new milestone by obtaining higher than 27 PSNR in the challenging two-view reconstruction task. On ACID dataset, HiSplat outperforms other methods by +0.5 PSNR than MVSpalt and +0.4 PSNR than TranSplat. For the patch-level SSIM and feature-level LPIPS metrics, HiSplat also gains significant improvement, suggesting that HiSplat can reconstruct details and large-scale structures with higher quality. Besides the performance, we also report the inference time and peak GPU memory in A.2.

**Table 1: Evaluation on RealEstate10K and ACID. Compared with the previous NeRF-based and generalizable Gaussian-Splatting-based method, HiSplat can consistently obtain higher rendering quality of unseen views.**
| Method | RealEstate10K | ACID |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| PSNR $\uparrow$ | SSIM $\uparrow$ | LPIPS $\downarrow$ | PSNR $\uparrow$ | SSIM $\uparrow$ | LPIPS $\downarrow$ |  |
| pixelNeRF Yu et al. () | 20.43 | 0.589 | 0.550 | 20.97 | 0.547 | 0.533 |
| GPNR Suhail et al. () | 24.11 | 0.793 | 0.255 | 25.28 | 0.764 | 0.332 |
| AttnRend Du et al. () | 24.78 | 0.820 | 0.213 | 26.88 | 0.799 | 0.218 |
| MuRF Xu et al. () | 26.10 | 0.858 | 0.143 | 28.09 | 0.841 | 0.155 |
| PixelSplat Charatan et al. () | 25.89 | 0.858 | 0.142 | 28.14 | 0.839 | 0.150 |
| MVSplat Chen et al. () | 26.39 | 0.869 | 0.128 | 28.25 | 0.843 | 0.144 |
| TranSplat Zhang et al. () | 26.69 | 0.875 | 0.125 | 28.35 | 0.845 | 0.143 |
| HiSplat(Ours) | 27.21 | 0.881 | 0.117 | 28.75 | 0.853 | 0.133 |

#### 4.2.2 Cross-dataset Generalization

To verify the generalization ability of HiSplat, we train the model on RealEstate10K and directly test it on DTU, ACID, and Replica in a zero-shot setting. As depicted in Figure 3, the images generated by HiSplat are more aligned with human perception, featuring less edge blurriness, less artefacts and more accurate location. The quantitive results are shown in Table 2. Compared with previous single-scale Gaussian-Splatting-based methods, HiSplat performs a significant generalization improvement, e.g., obtaining +1.05 PNSR on object-centric dataset DTU, +0.55 PSNR on outdoor dataset ACID and +3.19 PSNR on indoor dataset Replica over the suboptimal methods, suggesting that HiSplat is more effectively deployed in the practical open-world scenario with various data distribution. It is worth noting that on ACID, HiSplat’s zero-shot performance even outperforms others trained specially on ACID significantly. The outstanding generalization ability can be explained from two aspects: 1) the hierarchical Gaussian representation can reconstruct scenes of vastly different scales simultaneously 2) the error-aware mechanism is scale-invariant, which can benefit consistently across a wide range of scenes.

**Table 2: Evaluation on cross-dataset generalization. We train models on RealEstate10K, and test them on object-centric dataset DTU, outdoor dataset ACID, and indoor dataset Replica in a zero-shot setting. Compared with previous methods, HiSplat can better handle various scenes with different distributions and scales.**
|  | RealEstate10K $\rightarrow$ DTU | RealEstate10K$\rightarrow$ACID | RealEstate10K$\rightarrow$Replica |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method | PSNR $\uparrow$ | SSIM $\uparrow$ | LPIPS $\downarrow$ | PSNR $\uparrow$ | SSIM $\uparrow$ | LPIPS $\downarrow$ | PSNR $\uparrow$ | SSIM $\uparrow$ | LPIPS $\downarrow$ |
| PixelSplat Charatan et al. () | 12.89 | 0.382 | 0.560 | 27.64 | 0.830 | 0.160 | 23.98 | 0.821 | 0.202 |
| MVSplat Chen et al. () | 13.94 | 0.473 | 0.385 | 28.15 | 0.841 | 0.147 | 23.79 | 0.817 | 0.165 |
| TranSplat Zhang et al. () | 14.93 | 0.531 | 0.326 | 28.17 | 0.842 | 0.146 | - | - | - |
| HiSplat(Ours) | 16.05 | 0.671 | 0.277 | 28.66 | 0.850 | 0.137 | 27.17 | 0.899 | 0.113 |

Figure: Figure 3: Qualitative comparison of generalization ability. For the scenes out of training distribution, HiSplat can generate higher-quality novel-view images. More comparison is provided in A.3.
Refer to caption: x3.png

### 4.3 Ablation Experiment

To verify the effectiveness of each proposed technique of HiSplat, we conduct ablation experiments to eliminate each component step by step, and compare them with previous methods on RealEstate10K. As illustrated in Table 3, different degrees of performance degradation are observed of any removal, validating the necessity of each component. It merits attention that the vanilla hierarchical 3D Gaussian representation cannot perform competitively compared with previous methods (-0.21 PSNR compared with MVSpalt). Whereas, when combined with the proposed EAM and MFM, the potential of hierarchical manner is unleashed with prominent improvement (+0.82 PSNR). The reason is that the EAM and MFM promote the transfer of error-aware information and features across different scales, driving Gaussians in the later stages to compensate for lacking details and repair the error of earlier stages for joint optimization, as stated in Sec 1.

**Table 3: The ablation study on RealEstate10K. Hier: Hierarchical Gaussian, EAM: Error Aware Module, MFM: Modulating Fusion Module, DINO: using DINOv2 feature. Only using vanilla hierarchical Gaussians cannot perform better than previous methods. Each proposed component contributes to the final superior results.**
|  | Hier | EAM | MFM | DINO | PSNR$\uparrow$ | SSIM$\uparrow$ | LPIPS$\downarrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | $\checkmark$ | $\times$ | $\times$ | $\times$ | 26.18 | 0.869 | 0.135 |
|  | $\checkmark$ | $\checkmark$ | $\times$ | $\times$ | 26.76 | 0.874 | 0.123 |
|  | $\checkmark$ | $\checkmark$ | $\checkmark$ | $\times$ | 27.02 | 0.879 | 0.120 |
| Variants of HiSplat(Ours) | $\checkmark$ | $\checkmark$ | $\checkmark$ | $\checkmark$ | 27.21 | 0.881 | 0.117 |
|  | PixelSplat Charatan et al. () | 25.89 | 0.858 | 0.142 |  |  |  |
| Other methods | MVSplat Chen et al. () | 26.39 | 0.869 | 0.128 |  |  |  |

### 4.4 Analysis of Hierarchical Gaussians

In this section, we visually analyze the different stages during the inference process to reveal the mechanism behind the effectiveness of HiSplat.

Analysis of 3D Gassian Primitives.
To visually display the fusing Gaussians in different stages, we take the center of Gaussian primitives as the 3D position of point clouds and utilize the color of corresponding context images to draw the Gaussian primitives. As shown in Figure 4, for the later stage, as the number of Gaussian primitives increases, the rendering quality also gradually improves, demonstrating the effectiveness of adding Gaussian primitives. To further analyze the function of adding Gaussians of each stage, we report the statistical probability of opacity and mean scale of Gaussians. It is noteworthy that because the fusing Gaussians in the later stage contain the Gaussians from the early stages, for a clear analysis, we individually report the statistical value of fusing Gaussians from each stage. It can be observed that the Gaussian primitives in stage 1 are sparse, solid, and large while the adding Gaussian primitives from later stages gradually become dense, transparent and small. This pattern of Gaussian attributes aligns with our hypothesis: larger and more solid Gaussian primitives form the skeleton of the scene, aiming to establish the large-scale basic structure, while smaller and more transparent Gaussian primitives serve as decoration, further refining the texture details, which can be described as “from bone to flesh”. Following this hypothesis, we explain the function of the displayed hierarchical Gaussians that Gaussian primitives from stage 1 and stage 2 are relatively solid, adhering closely to the surface of the scene, which gradually completes the outline and basic structure; as for the Gaussian primitives from stage 3, they are very tiny and transparent, introducing richer texture details and illumination. Besides, compared with other methods, HiSplat can render a high-quality novel-view image with more accurate and consistent geometry, especially the toy bird’s crest in Figure 4.

Analysis of 2D Error Map. To analyze how hierarchical Gaussians reconstruct the scene step by step in detail, we show the colored error map of different stages, generated by comparing the rendering images with the ground truth in Figure 5. The brighter and redder pixels exhibit more significant errors and a rectangle highlights the regions with complex and rich textures. It can be observed that as the stage index increases, the overall error continuously decreases, especially in areas with complex textures. Besides, by directly examining the rendered images, we can observe that there is less blurriness and more details. It suggests that HiSplat can perceive the error in the early stage, and utilize Gaussians in the later stage for repair and compensation.

Figure: Figure 4: Comparison of Gaussian primitives in different stages on DTU. HisPlat can gradually generate large-scale solid Gaussians as “bone” and small-scale transplant Gaussians as “flesh”, confirming better rendering quality and geometry.
Refer to caption: x4.png

Figure: Figure 5: Comparison of rendering images from different stages on RealEstate10K. HiSplat can perceive the error, and utilize Gaussians in the later stages to add details and correct errors gradually.
Refer to caption: x5.png

## 5 Conclusion

In this paper, we propose a novel generalizable 3D Gaussian Splatting framework, HiSplat, aiming to render novel-view images from sparse (only two) context images. Different from previous methods predicting single-scale Gaussians, HiSplat gradually generates multi-scale hierarchical Gaussians to reconstruct the large-scale structure and texture details with higher quality. To facilitate the information interaction of different stages, we propose Error Aware Module and Modulating Fusion Module for Gaussian compensation and repair. Extensive experiments across multiple datasets demonstrate that HiSplat significantly enhances the quality of reconstructions and cross-dataset generalization, surpassing previous single-scale methods.

## Appendix A Appendix

### A.1 Detailed architecture and experiment setting

In this section, we provide more details of the architecture and experiments.

More Architecture details. For the U-Net backbone, the encoder and decoder are constructed with a similar architecture consisting of stacked residual blocks. The kernel size of each block is $3\times 3$, with increasing channels $[32,64,128]$ and half resolution for each stage. For combining the U-Net features with DINOv2 features, we follow the Side View Attention of MVSformer++ Cao et al. () to further improve the information aggregation from different views. There is a convolution layer to align the channels of DINOv2 features with the corresponding U-Net features for addition. Then, another convolutional layer is used to generate the features of each stage.

More Experiment details.
For the training schedule on ACID and RealEstate10K, we follow Chen et al. () to set a learning rate of 0.0002 with warm-up cosine decay. The warm-up steps are 2000. Random horizontal flipping is used as data augmentation for each image set. All experiments are based on the deep learning framework PyTorch and utilize Pytorch-Lightning for better organization. For results on RealEstate10K and ACID, because our basic experiment setting is as same as Chen et al. (); Charatan et al. (); Zhang et al. (), we directly cite the results from the original literature. For the zero-shot results on Replica, we only conduct experiments to compare with open-source methods MVSplat and PixelSplat, using their latest official checkpoints.

### A.2 Efficiency and Effectiveness of different stages

To report the effectiveness and efficiency of the proposed HiSplat, we test the inference cost and performance of HiSplat and previous methods. All experiments are conducted on an RTX4090 with batch size 8. The input image size is $256\times 256$, and the reported inference time and peak memory are the average value of 10,000 recorded data points with the same inference settings. Because the early-stage Gaussians are predicted without relying on the later-stage Gaussians, HiSplat can terminate the inference in the early stages and reduce the inference cost, we add two variants of HiSplat, HiSplat-Stage 1 and HiSplat-Stage 2, which only predict the Gaussians in the stage 1 and 2, respectively. Thanks to the hierarchical character of HiSplat, it is very simple to change the mode of HiSplat for a flexible implementation. The results are shown in Table 4. It can be observed that compared with the previous methods, the complete version of HiSplat (HiSplat-Stage 3) can obtain the best performance (+0.82 PSNR compared with MVSplat) with relatively high but bearable inference cost, due to the three-stage processing and the introduction of DINOv2 feature. Flash Attention Dao (); Dao et al. () and stage-wise parallel techniques are the optimal methods for future cost reduction. Besides, other modes of HiSplat can properly sacrifice the performance for lightweight implementation. Thus, compared with the previous fixed methods, HiSplat has the potential to form a dynamic framework for various practical requirements of resource or quality, suggesting HiSplat can achieve a better balance between performance and inference cost.

**Table 4: The efficiency and effectiveness of HiSplat and other methods. HiSplat-Stage 1,2 represents only forwarding the 1,2 stages of HiSplat to generate part of hierarchical Gaussians for rendering, which can sacrifice performance for higher efficiency. Under different resource constraints, HiSplat can switch inference mode flexibly, achieving a good balance between performance and inference cost.**
| Method | Peak Memory/GB$\downarrow$ | Inference Time/s$\downarrow$ | PSNR$\uparrow$ | SSIM$\uparrow$ | lPIPS$\downarrow$ |
| --- | --- | --- | --- | --- | --- |
| PixelSplat Charatan et al. () | 24.17 | 1.47 | 25.89 | 0.858 | 0.142 |
| MVSplat Chen et al. () | 14.08 | 0.27 | 26.39 | 0.869 | 0.128 |
| HiSplat-Stage 1 | 12.91 | 0.24 | 26.13 | 0.858 | 0.141 |
| HiSplat-Stage 2 | 14.74 | 0.36 | 26.99 | 0.879 | 0.120 |
| HiSplat-Stage 3 | 23.34 | 0.51 | 27.21 | 0.881 | 0.117 |

### A.3 More qualitative comparison

For a more detailed visual comparison, there are extended qualitative comparisons including novel-view-synthesis results on RealEstate10K in Figure 6; zero-shot results on DTU in Figure 7, ACID in Figure 8, Replica in Figure 9.

Figure: Figure 6: Extended qualitative comparison on RealEstate10K. Compared with the previous methods, HiSplat can handle large-scale structures (the location or shape of objects) and texture details better.
Refer to caption: x6.png

Figure: Figure 7: Extended qualitative comparison on DTU.For unseen objects, HiSplat can reconstruct the texture details more clearly with fewer artefacts and less blurriness.
Refer to caption: x7.png

Figure: Figure 8: Extended qualitative comparison on ACID. For unseen outdoor coastal scenes, HiSplat can generate more accurate illumination and location with fewer artefacts.
Refer to caption: x8.png

Figure: Figure 9: Extended qualitative comparison on Replica. For unseen indoor scenes, HiSplat can reconstruct the details better with less blurriness and fewer artefacts.
Refer to caption: x9.png

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. Discuss more about the importance of incorporating the DINO feature.
2. Discuss more about the complexity analysis of the method.
soundness: 3
strengths: 1. HiSplat first introduces the hierarchical 3D Gaussian representation in the task of generalizable 3D Gaussian Splatting.
2. The experiment effectively demonstrated the effectiveness of its hierarchical representation through Fig 4, which shows the primitives of Gaussians obtained from different stages.
3. This paper show the effective ablation study, comparing the impact of different modules on the performance of Generalizable Sparse-View Reconstruction.
summary: HiSplat introduces a coarse-to-fine strategy to construct hierarchical 3D Gaussians for generalizable 3D Gaussian Splatting. Additionally, HiSplat proposes an Error-Aware Module and a Modulating Fusion Module to enhance inter-scale interactions. Comprehensive experiments demonstrate that HiSplat achieves better results than single-scale methods.
weaknesses: 1. Several spelling errors have been identified in the document, specifically on lines 73, 106, and 508.
2. The hierarchical representations appear to be memory-intensive and computationally inefficient. It is recommended that the paper discusses the implications for memory, FLOPs, and inference time.
3. The rationale for incorporating the DINO feature seems underwhelming, and the provided experiments do not convincingly demonstrate the necessity of including the DINO feature.
4. I hope to see additional experiments conducted to explore the impact of increasing the number of perspectives on the quality of reconstruction. It appears that the majority of the experiments presented in the article are limited to just two perspectives, which may not fully capture the potential benefits of a multi-perspective approach.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: Apart from the weakness part, I also have the following questions.
1. Will the number of Gaussian primitives change significantly after applying this hierarchical structure? A comparison with baseline methods on the primitive numbers will be nice.
2. It seems that the first example in Fig.6. shows that HiSplat can generate less artifacts in the occluded area, as highlighted by the red bounding box. Does this just happen in coincidence or is it a prevalent phenomenon?

I still have the aforementioned concerns and I would be glad to raise my rating if they can be addressed.
soundness: 2
strengths: 1. The idea of using different hierarchical Gaussians to simultaneously capture large-scale structures and delicate textures in scene generation sounds reasonable.
2. The ablative study seems complete. 
3. The motivation is clear since not all points in a single image are equally important.
summary: This paper claims that previous generalizable 3D Gaussian Splatting methods utilize uniform 3D Gaussians, making it challenging to simultaneously capture large-scale structures and intricate texture details. The authors propose a hierarchical approach to generalizable 3D Gaussian Splatting, constructing hierarchical 3D Gaussians through a coarse-to-fine strategy. Additionally, they design an Error Aware Module and a Modulating Fusion Module to manage interactions among different hierarchical Gaussians.
weaknesses: 1. The paper introduces many modules, which slows down the process compared to MVSplat, which is also revealed in Tab.4, after adding all the stages together. 
2. The improvement in metrics compared with baseline method does not seem very significant from the reviewer's perspective. But the generalization capability to unseen datasets like DTU seems interesting. The authors can show more cases that demonstrates generalization capability. 
3. Some of the visualized cases does not seem to possess a very significant improvement compared with MVSplat. For example, the last line of Fig.6; the left red bounding box in the first example in Fig. 1.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 4
questions: 1. Since it is unlikely we can always get accurate camera poses from sparse views in real life, can this work be extended to handle the pose-free sparse-view reconstruction for the captures from real life? If it is easy to implement, it would be great to see some examples.
2. The depth coefficient $\eta$ is a bit tricky. I assume it is a fixed value for each stage after the training, is this correct? If so, how do you choose the proper $\eta$ values?
soundness: 3
strengths: 1. The paper is well-motivated from the prior works on multi-scale hierarchical visual knowledge. It is well-written.
2. The way the authors integrate the multi-scale features is clever and interesting. I love the Error Aware part, HiSplat wisely incorporates the reconstruction error refinement, usually through gradient optimizations, into this feed-forward reconstruction model.  The error maps are used to guide the refinement on the higher levels. I believe this idea can also be beneficial for other related tasks.
3. The analysis in 4.4 is very informative and useful. The figures effectively demonstrate how multi-scale features can help improve the reconstruction quality.
summary: This work presents a new 3DGS-based generalizable sparse-view scene reconstruction method, HiSplat, which effectively integrates multi-scale hierarchical MVS features with proposed EAM and MFM modules to enhance the quality of feed-forward 3DGS models, especially in fine-grained details. Experimental results demonstrate the superior reconstruction quality compared to prior SoTA methods.
weaknesses: 1. The current model is pretty complex. It contains a lot of different neural models (MVSformer++, DINOv2, and a bundle of UNets and MLPs). I am not very clear which modules are trainable and which are not. The authors should clarify and summarize this in 3.6. Also, it would be even better, this is not required for rebuttal, if HiSplat could be a relatively simpler unified foundation model (e.g., like LRM).  
2. Although the writing is generally good, the citation format of this paper is a bit disturbing. Please properly use `\cite` and `\citep` provided in the ICLR template.
3. Minor typos:
    * MVSpalt → MVSplat
    * L298: fellow → follow

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: See weaknesses.
soundness: 4
strengths: - The proposed Error Aware Module and Modulating Fusion Module are interesting, which can effectively filter the errors from photometric loss in a simple way.

- Experiments are extensive. Both qualitative and quantitative results show that HiSplat achieves SOTA performance compared to the compared baselines. The results are convincing.

- The paper is easy to follow.
summary: This paper proposes HiSplat to estimate the 3D Gaussian Splatting structure of two given images with known camera poses in a feedforward process. It aims to use multi-scale information to improve the quality and for which two novel modules are proposed. Extensive experiments on various datasets can show its SOTA performance and good generalizability, and validate the effects of each proposed component.
weaknesses: - Some related methods are missed in the discussion, e.g., FreeSplat [1] and Splatt3R [2]. Especially, FreeSplat also uses a cost-volume-based structure to estimate the depth and then Gaussians with multi-scale strategy. A step further, the architecture of this work is also somehow similar to CasMVS [3] that uses a cascade structure to solve the MVS depth estimation task. The main differences lie in how the multi-scale features are used. Need more deeper discussions about these related works to show the technical differences and more valuable analysis.

- The setting of this paper seems to be fixed at using only two images. Would like to see some discussion about if it's possible to extend the method to more input views, just like what FreeSplat does. Even if it is a part of limitation, it would still be much valuable.



[1] Wang, Yunsong, et al. "FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenes." arXiv preprint arXiv:2405.17958 (2024).

[2] Smart, Brandon, et al. "Splatt3r: Zero-shot gaussian splatting from uncalibrated image pairs." arXiv preprint arXiv:2408.13912 (2024).

[3] Gu, Xiaodong, et al. "Cascade cost volume for high-resolution multi-view stereo and stereo matching." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.

Meta-review: This paper introduces a coarse-to-fine strategy to construct hierarchical 3D Gaussians for generalizable sparse-view 3D Gaussian Splatting. In addition, this paper proposes an Error-Aware Module and a Modulating Fusion Module to promote inter-scale interactions. Comprehensive experiments demonstrate that the proposed method achieves better results than single-scale methods.

Main strengths of this paper are as below: 
- The paper is clearly written and easy to follow. 
- The paper is well-motivated from the prior works on multi-scale hierarchical visual knowledge. 
- The paper introduces a coarse-to-fine strategy into sparse-view Gaussian Splatting. And it proposes an Error-Aware Module and a Modulating Fusion Module to promote inter-scale interactions, which are innovative and inspiring.
- The experiments demonstrate improvements over previous methods. And the ablation study shows the effectiveness of Error Aware Module and Modulating Fusion Module.

Needing more experiments and analyses were raised by the reviewers. Please revise the paper according to the discussions before submitting the final version.

Decision: Accept (Poster)