SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Here,  $i$  represents the positional encoding in the  $i_{th}$  dimension and  $\beta$  is a scale hyperparameter we empirically set to 100. The updated formula successfully solves the misalignment of traditional positional encoding, results are shown in Tab. 4. The specific proofs will be included in the Appendix section.
3.4 Hybrid Extractor
In our learning-based rendering, a finer level of visual feature is necessary, which is not achieved by traditional feature extractors that rely on high-level features obtained through FPN. These features
Figure 3: Comparison of the original extractor used in [8, 9] (top) and our hybrid extractor (bottom). The original extractor primarily discerns high-level features, demonstrating less efficacy. In contrast, our hybrid extractor excels in integrating multi-level features, demonstrating superior efficacy.
are highly semantic abstract and not suitable for low-level visual feature matching [49]. To overcome this limitation, inspired by NeuralRecon [50], we further propose Hybrid Extractor. Rather than relying on FPN to generate one feature map from high-level features of CNN, and using a 3D U-Net to process projected features as shown in Fig. 3, we leverage all level features from various layers to construct multi-level volume features. Then, we adopt a 3D CNN decoder to fuse and decode the multi-level volume features, producing the final global volume features.
Our approach enables us to perceive both low and high-level features, which is crucial for generalizable neural surface reconstructions that require detailed surface processing. Second, by avoiding the use of the encoder part of the 3D U-Net, we reduce the computational complexity and allow us to build a higher resolution volume feature within the same computational budget.
3.5 Loss Functions
Our overall loss function is defined as the weighted sum of two loss terms:
$$
\mathcal {L} = \mathcal {L} _ {\text {r e n d e r i n g}} + \alpha \mathcal {L} _ {\text {d e p t h}}, \tag {13}
$$
where  $\mathcal{L}_{\mathrm{rendering}}$  constrains the observed colors to match the ground truth colors and is formulated as:
$$
\mathcal {L} _ {\text {r e n d e r i n g}} = \frac {1}{S} \sum_ {s = 1} ^ {S} \| C (\mathbf {r}) - C _ {g} (\mathbf {r}) \| _ {2}, \tag {14}
$$
Here,  $S$  is the number of sampled rays for training, and  $C_g(\mathbf{r})$  represents the ground truth color of the sample ray  $r$ . The depth loss  $\mathcal{L}_{\mathrm{depth}}$  is defined as
$$
\mathcal {L} _ {\text {d e p t h}} = \frac {1}{S _ {1}} \sum_ {s = 1} ^ {S _ {1}} | D (\mathbf {r}) - D _ {g} (\mathbf {r}) |, \tag {15}
$$
where  $S_{1}$  is the number of pixels with valid depth and  $D_{g}(\mathbf{r})$  is the ground truth depth. In our experiments, we set  $\alpha = 1.0$ .
Datasets. The DTU dataset [10] is a large-scale indoor multi-view stereo dataset consisting of 124 different scenes captured under 7 different lighting conditions. To train our frameworks, we adopted the same approach as in previous works [9, 8]. Furthermore, we evaluated our models' generalization capabilities by testing them on three additional datasets: Tanks & Templates [12], ETH3D [13], and BlendedMVS [11], where no additional training was performed on the testing datasets.
Figure 4: Sparse view reconstruction on testing scenes in the DTU [10]. Comparison with VolRecon [8] (left), our proposed ReTR (right) renders a more accurate surface and preserves finer details, e.g. the window of the house (scan 24), the nose of smurf, ReTR produces much sharper details. Best viewed on a screen when zoomed in.
SCAN	Mean↓	24	37	40	55	63	65	69	83	97	105	106	110	114	118	122
COLMAP [16]	1.52	0.90	2.89	1.63	1.08	2.18	1.94	1.61	1.30	2.34	1.28	1.10	1.42	0.76	1.17	1.14
MVSNet [51]	1.22	1.05	2.52	1.71	1.04	1.45	1.52	0.88	1.29	1.38	1.05	0.91	0.66	0.61	1.08	1.16
IDR [27]	3.39	4.01	6.40	3.52	1.91	3.96	2.36	4.85	1.62	6.37	5.97	1.23	4.73	0.91	1.72	1.26
VoISDF [1]	3.41	4.03	4.21	6.12	0.91	8.24	1.73	2.74	1.82	5.14	3.09	2.08	4.81	0.60	3.51	2.18
UNISURF [3]	4.39	5.08	7.18	3.96	5.30	4.61	2.24	3.94	3.14	5.63	3.40	5.09	6.38	2.98	4.05	2.81
NeuS [2]	4.00	4.57	4.49	3.97	4.32	4.63	1.95	4.68	3.83	4.15	2.50	1.52	6.47	1.26	5.57	6.11
PixelNeRF [32]	6.18	5.13	8.07	5.85	4.40	7.11	4.64	5.68	6.76	9.05	6.11	3.95	5.92	6.26	6.89	6.93
IBRNet [30]	2.32	2.29	3.70	2.66	1.83	3.02	2.83	1.77	2.28	2.73	1.96	1.87	2.13	1.58	2.05	2.09
MVSNeRF [31]	2.09	1.96	3.27	2.54	1.93	2.57	2.71	1.82	1.72	2.29	1.75	1.72	1.47	1.29	2.09	2.26
SparseNeuS [9]	1.96	2.17	3.29	2.74	1.67	2.69	2.42	1.58	1.86	1.94	1.35	1.50	1.45	0.98	1.86	1.87
VolRecon [8]	1.38	1.20	2.59	1.56	1.08	1.43	1.92	1.11	1.48	1.42	1.05	1.19	1.38	0.74	1.23	1.27
ReTR (Ours)	1.17	1.05	2.31	1.44	0.98	1.18	1.52	0.88	1.35	1.30	0.87	1.07	0.77	0.59	1.05	1.12
Table 1: Quantitative results of sparse view reconstruction on 15 testing scenes of DTU dataset [10]. We report the chamfer distance, the lower the better, Methods are split into four categories from top to bottom: a) MVS-based methods, b) Per-scene optimization methods, c) Generalizable rendering methods, and d) Generalizable reconstruction methods. The best scores are in bold and the second best are in underlined.
Baselines. To demonstrate the effectiveness of our method from various perspectives, we compared it with (1) SparseNeus [9] and VolRecon [8], the state-of-the-art generalizable neural surface reconstruction method; (2) Generalizable neural rendering methods (3) Neural implicit reconstruction [27, 2, 1, 3] which require individual training for each scene from scratch. (4) Popular multi-view stereo (MVS) [16, 51] methods. Further details on the baselines are provided in the appendix.
4.1 Sparse View Reconstruction
For comparison, we performed sparse reconstruction using only three views, following the same approach as [8, 9]. We adopted the same evaluation process and testing split as used in previous works [8, 9] to ensure a fair comparison. We use a similar approach as VolRecon [8] to generate mesh, more details can be found in Appendix. As shown in Tab. 1, our method outperforms VolRecon [8] and SparseNeuS [9] by a significant margin. Moreover, our method also outperforms popular MVS methods such as MVSNet [51]. Furthermore, we present the qualitative results of sparse view reconstruction in Fig. 4. Our reconstructed geometry exhibits smoother surfaces and less noise compared to the current SoTA methods.
4.2 Depth Map Evaluation & Full View Reconstruction
We compare our rendered depth with those generated by SparseNeuS [9], MVSNet [51] and VolRecon [8]. Following the experiment settings introduced in VolRecon [8], we also use four source views as input for depth rendering. Additionally, we evaluated the performance by fusing all depth maps into a global point cloud. As shown in Tab. 2, our method outperforms existing methods in both evaluations. Moreover, as demonstrated in Fig. 5, our method achieves a sharper boundary with less noise and fewer holes compared to the current SoTA method [8].
VolRecon
ReTR(ours)
Figure 5: Full view reconstruction visualization in test set of DTU [10], comparison with VolRecon [8] (left), our proposed ReTR (right) reconstructs better point clouds, e.g. fewer holes, the skull head top, and the house roof gives a much complete representation, e.g. finer details, the house window, and the skull cheek, provides much finer details. Best viewed on a screen when zoomed in.
ReTR(ours)
Method	Acc. ↓	Comp. ↓	Chamfer ↓	<1mm ↑	<2mm ↑	<4mm ↑
MVSNet [51]	0.55	0.59	0.57	29.95	52.82	72.33
SparseNeuS [9]	0.75	0.76	0.76	38.60	56.28	68.63
VoilRecon [8]	0.55	0.66	0.60	44.22	65.62	80.19
ReTR (Ours)	0.54	0.51	0.52	45.00	66.43	81.52
4.3 Generalization
To evaluate the generalization ability of our model without retraining, we use three datasets, namely Tank & Temples, BlendedMVS, and ETH3D [12, 11, 13]. The high-quality reconstruction of large-scale scenes and small objects in different domains, as shown in Fig. 6, demonstrates the effectiveness of our method in terms of generalization capability.
We conduct ablation studies to examine the effectiveness of each module in our design. The ablation study results are reported on sparse view reconstruction in test split following SparseNeuS [9] and VolRecon [8].
Effectiveness of Modules. We evaluate key components of our approach to generalizable neural surface reconstruction, as shown in Tab. 3. For the evaluation of the occlusion transformer, we keep the original transformer architecture while removing the special design we proposed in Sec. 3.3, to ensure the training parameter would not affect the evaluation. For the hybrid extractor part, we replace this module with the original extractor that has been used in [8]. Our results demonstrate that our approach can better aggregate features from different levels and use the highlight the importance of these components in our appro
Table 2: Quantitative results of full view reconstruction on 15 testing scenes of DTU dataset [10]. For the Accuracy (ACC), Completeness (COMP), and Chamfer Distance, the lower is the better. For depth map evaluation, threshold percentages (<1mm, <2mm, <4mm) are reported in percentage (%). The best scores are in bold.
Reder Trans.	Occ. Trans.	Hybrid Ext.	Chamfer↓
✓	×	×	1.31
✓	×	✓	1.29
✓	✓	×	1.28
✓	✓	✓	1.17
Table 3: Model component ablation. All of these parts are described in Sec. 3.3.
Robustness of Different Sampling. Tab. 4 displays the effects of altering the number of sample points on the reconstruction quality of VolRecon[8] and ReTR. Our method surpasses the current SoTA, even when the number of sampling points decreases. These results suggest that existing methods that rely on sampling points of the ray struggle to provide confident predictions of the surface due to the nature of volume rendering. Our approach, which uses learning-based rendering, is more resilient to sampling strategies and can provide reliable depth estimations even with fewer samples. Meanwhile, the effectiveness of continuous P.E. in Sec. 3.3 is proved through the result.
Unsupervised Neural Surface Reconstruction. Our approach is still applicable to unsupervised neural surface reconstruction using only colors for training, which remove  $\mathcal{L}_{\mathrm{depth}}$ . Meanwhile, we find
Figure 6: Generalize reconstruction visualization in 3 datasets BlendedMVS, ETH3D, and Tank & Temples [11, 10, 13], comparison with VolRecon [8] (middle), our proposed ReTR (right) generalize well to the large-scale datasets without fine-tuning. ReTR produces finer details and sharper boundaries. Best viewed on a screen when zoomed in.
Sample Points	VolRecon [8]	ReTR (Ours*)	ReTR (Ours)
(16, 16)	1.60	1.59	1.30
(32, 32)	1.45	1.42	1.20
(64, 0)	1.45	1.26	1.22
(128, 0)	1.79	1.46	1.20
(64, 64)	1.38	1.18	1.17
Table 4: Number of sampling ablation. The * denotes our model using traditional positional encoding.
Method	Training Losses	Chamfer ↓
SparseNeuS [9]	wLdepth	4.22
w/o Ldepth	1.96
VolRecon [8]	wLdepth	1.38
w/o Ldepth	2.06
ReTR (Ours)	wLdepth	1.17
w/o Ldepth	1.45
Table 5: Ablation study of training losses.
that our method significantly outperforms the current SoTA method under unsupervised situations and is even comparable to COLMAP [16] the popular MVS technique, As shown in Tab. 5, This is further evidence that the improvement of complex rendering systems for implicit reconstruction is huge.
We have proposed Reconstruction Transformer (ReTR), a novel framework for generalizable neural surface reconstruction that uses transformers to model complex rendering processes. ReTR represents a significant advancement in the field of surface reconstruction, offering a powerful solution to the challenges faced by neural implicit reconstruction methods. Additionally, we delve into the design procedure of learning-based rendering. This exploration broadens our understanding of enhancing complex rendering systems and sets the stage for future research endeavors not only in surface reconstruction but also in other tasks relative to differentiable rendering.
We thank Shuai Yang and Wenhang Ge for the thoughtful review of our manuscript and valuable discussions throughout this project. Thank you to Yukang Chen, Shuhan Zhong and Jierun Chen for ideas and feedbacks on our manuscript. We would also like to thank the Turing AI Computing Cloud (TACC) [52] and HKUST iSING Lab for providing us with computation resources on their platform.
[51] Y. Yao, Z. Luo, S. Li, T. Fang, and L. Quan, "Mvsnet: Depth inference for unstructured multiview stereo," in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 767-783.
[52] K. Xu, X. Wan, H. Wang, Z. Ren, X. Liao, D. Sun, C. Zeng, and K. Chen, "Tacc: A full-stack cloud computing infrastructure for machine learning tasks," arXiv preprint arXiv:2110.01556, 2021. [Online]. Available: https://arxiv.org/abs/2110.01556
A.1 Experimental Environment
Software and hardware environment:
- CUDA version: 11.1
cuDNN version: 8.0.5
PyTorch version: 1.10.1
- GPU: Nvidia RTX 3090
- CPU: Intel Xeon Platinum 8180 @ 2.50 GHz × 2
A.2 Training Detail
Our model is implemented in PyTorch using the PyTorch Lightning framework. During the training stage, we resize the input image to  $640 \times 512$  and the source views to  $N = 4$ . To train our model, we employ the Adam optimizer on a single Nvidia 3090 GPU. Initially, the learning rate is set to  $10^{-4}$  and gradually decays to  $10^{-6}$  using a cosine learning rate scheduler. Throughout the training, we use a batch size of 2 and set the number of rays to 1024. To enhance the sampling strategy, we apply a coarse-to-fine approach with both  $N_{coarse}$  and  $N_{fine}$  set to 64. The  $N_{coarse}$  points are uniformly sampled between the near and far plane, while the  $N_{fine}$  points are sampled using importance sampling based on the coarse probability estimation. Regarding the global feature volume  $f^{v}$ , we set its resolution to  $\mathrm{K} = 128$ . For inference on DTU, the image resolution is set to  $800 \times 600$ . For datasets such as BlendedMVS [11], ETH3D [13], and Tanks & Temples [12], we maintain the original image resolution. Training our model requires approximately 3 days on a single Nvidia 3090 GPU. Moreover, when constructing larger models such as the large and xlarge models by stacking more layers, the training time will naturally increase due to the increased model size.
A.3 Mesh and Point Cloud Generation
Following the settings employed in VolRecon [8], we generate depth maps from virtual viewpoints by shifting the original camera along its  $x$ -axis by  $d = 25$  mm. Subsequently, we perform TSDF fusion and applied the Marching Cubes algorithm to merge all the rendered depths into a voxel grid with a resolution of  $1.5$  mm and extract a mesh representation. For point cloud generation, we initially generate 49 depth maps by leveraging the four nearest source views. These 49 depth maps are then fused together to form a unified point cloud.
B Technical Details and Discussion
B.1 Discussion of Hitting Probability
The attention score in ReTR can be interpreted as the probability of a ray being hit. However, when using softmax, the attention scores for each ray are forced to sum up to 1, implying that every ray should be considered a hit. To gain further insights, we examine the distribution of attention scores for rays that are not hitting. Figure 7 illustrates the results, demonstrating that the transformer intelligently employs a wider distribution to model rays that do not hit. The underlying
Figure 7: Hitting probability comparison.
rationale is that the transformer treats the surrounding air as a medium that contributes to the color.
Sample Points	scan 24	scan 37	scan 40	scan 55	scan 63	scan 65	scan 69	scan 83	scan 97	scan 105	scan 106	scan 110	scan 114	scan 118	scan 122
16+16	1.09	2.49	1.51	1.09	1.62	1.64	0.97	1.35	1.43	1.05	1.21	0.77	0.72	1.27	1.28
32+32	1.06	2.30	1.46	0.97	1.35	1.53	0.89	1.38	1.34	0.92	1.10	0.74	0.60	1.10	1.17
64+0	1.11	2.39	1.43	1.06	1.36	1.62	0.94	1.28	1.31	0.91	1.12	0.78	0.64	1.18	1.20
128+0	1.39	2.36	1.54	1.01	1.18	1.65	0.97	1.26	1.26	0.83	1.10	0.84	0.62	1.09	1.15
Table 6: Chamfer distance of a number of different sampling points, results are shown for each scan under different settings.
When a ray does not hit, the transformer aggregates information from the surrounding air to obtain the color from these mediums.
B.2 Hierarchical Volume Sampling through Attention Map
Given that our framework does not incorporate weights as seen in traditional frameworks like NeRF or NueS, we refine the original hierarchical sampling strategy by substituting the weights with the attention scores of each point. This approach, as discussed in the main text, is both straightforward and impactful. Additionally, we highlight that our method exhibits greater robustness in terms of the number of sampling points compared to the current state-of-the-art techniques, thereby offering an additional advantage within our framework.
B.3 Continuous Positional Encoding Proof
To imbue our system with positional awareness of actual distance, we initially derive the formula for the attention score, denotes as  $s$  of features in  $\pmb{x}_i$  and  $\pmb{x}_j$ :
$$
s = \left(\mathbf {f} _ {i} ^ {f} + \mathbf {p} _ {i}\right) W _ {q} W _ {k} ^ {\top} \left(\mathbf {f} _ {j} ^ {f} + \mathbf {p} _ {j}\right) ^ {\top}, \tag {16}
$$
where  $\mathbf{p}$  represents the positional encoding in  $\pmb{x}$ . We subsequently expand Eq. (16) as follows:
$$
s = \left(\mathbf {f} _ {i} ^ {f}\right) W _ {q} W _ {k} ^ {\top} \left(\mathbf {f} _ {j} ^ {f}\right) ^ {\top} + \left(\mathbf {f} _ {i} ^ {f}\right) W _ {q} W _ {k} ^ {\top} \left(\mathbf {p} _ {j}\right) ^ {\top} + \left(\mathbf {p} _ {i}\right) W _ {q} W _ {k} ^ {\top} \left(\mathbf {f} _ {j} ^ {f}\right) ^ {\top} + \left(\mathbf {p} _ {i}\right) W _ {q} W _ {k} ^ {\top} \left(\mathbf {p} _ {j}\right) ^ {\top}, \tag {17}
$$
where the fourth component of Eq.(17) denotes the interaction between two locations, and  $W_{q}W_{k}^{\top}$  represents the trainable parameters. To ensure our MLP actual positional awareness, we need to make the function satisfy the following conditions:
$$
\left(\mathbf {p} _ {i}\right) \left(\mathbf {p} _ {j}\right) ^ {\top} = f \left(t _ {j} - t _ {i}\right), \tag {18}
$$
where  $t_j - t_i$  denotes the distance between  $x_i$  and  $x_j$ . When we apply our positional encoding (PE), the fourth component of Eq.(17) can be simplified as:
$$
\begin{array}{l} (\mathbf {p} _ {i}) (\mathbf {p} _ {j}) ^ {\top} = \left[ \sin \left(\beta t _ {i} / 1 0 0 0 0 ^ {2 i / D}\right), \cos \left(\beta t _ {i} / 1 0 0 0 0 ^ {2 i / D}\right) \right] \tag {19} \\ \times \left[ \sin \left(\beta t _ {j} / 1 0 0 0 0 ^ {2 i / D}\right), \cos \left(\beta t _ {j} / 1 0 0 0 0 ^ {2 i / D}\right) \right] ^ {\top}, \\ \end{array}
$$
$$
\begin{array}{l} \left(\mathbf {p} _ {i}\right) \left(\mathbf {p} _ {j}\right) ^ {\top} = \sin \left(\beta t _ {i} / 1 0 0 0 0 ^ {2 i / D}\right) \sin \left(\beta t _ {i} / 1 0 0 0 0 ^ {2 i / D}\right) \tag {20} \\ + \cos (\beta t _ {j} / 1 0 0 0 0 ^ {2 i / D}) \cos (\beta t _ {j} / 1 0 0 0 0 ^ {2 i / D}). \\ \end{array}
$$
By applying the sum-to-product identities, we obtain:
$$
\left(\mathbf {p} _ {i}\right) \left(\mathbf {p} _ {j}\right) ^ {\top} = \cos \left(\beta \left(t _ {j} - t _ {i}\right) / 1 0 0 0 0 ^ {2 i / D}\right), \tag {21}
$$
where  $(t_j - t_i)$  represents the actual distance between  $\pmb{x}_i$  and  $\pmb{x}_j$ . Thus, continuous positional encoding enables the attainment of actual positional awareness.
Here we show additional experiment results:
C.1 Visualization Supplementary
Due to space limitations, we provide additional visual results for the experiments in this section. Specifically, we present the results for sparse view reconstruction in Fig. 9 and full view reconstruction of the point cloud in Fig. 11. Furthermore, we include the per-scene results for the number of sampling points in Tab. 6.
Models	Mean	scan 24	scan 37	scan 40	scan 55	scan 63	scan 65	scan 69	scan 83	scan 97	scan 105	scan 106	scan 110	scan 114	scan 118	scan 122
ReTR-B	1.17	1.05	2.31	1.44	0.98	1.18	1.52	0.88	1.35	1.30	0.87	1.07	0.77	0.59	1.05	1.12
ReTR-L	1.16	0.98	2.26	1.59	1.00	1.14	1.56	0.90	1.35	1.26	0.86	1.06	0.78	0.57	1.01	1.07
ReTR-XL	1.15	0.96	2.26	1.64	0.94	1.19	1.59	0.86	1.32	1.25	0.85	1.02	0.75	0.55	1.02	1.11
Table 7: Result of ReTR-Base, ReTR-Large and ReTR-XLarge evaluated on DTU under 3 views setting. We report chamfer distance, the lower the better.
Method	PSNR↑	MSE↓	SSIM↑	LPIPS↓
MVSNeRF*	25.92	0.003	0.89	0.19
VolRecon*	23.37	0.004	0.80	0.30
ReTR-B	25.88	0.004	0.83	0.28
ReTR-L	26.03	0.003	0.84	0.27
ReTR-XL	26.33	0.003	0.84	0.27
Table 8: Novel View synthesis result on DTU, * denotes our reproduced result.
C.2 Error Bar of ReTR
In order to assess the reproducibility and robustness of our model, we conduct three separate training runs using different random seeds. The corresponding results are presented in Figure 8. These results demonstrate that our model exhibits consistent performance across multiple training runs, indicating good reproducibility. Moreover, the minimal variance observes in the results further underscores the robustness of our model.
C.3 Effectiveness of Stacking Transformer Blocks
To explore the potential of simulating more complex light transport effects, we extend our learnable rendering approach by stacking multiple layers of transformer blocks. Specifically, we introduce two variations: ReTR-L, where we stack two transformer blocks, and ReTR-XL, where we stack three transformer blocks. This allows us to experimentally evaluate the effectiveness of a more complex rendering system. The results of these experiments are summarized in Tab. 7. The findings indicate that by overlaying multiple layers of transformers, we can simulate complex lighting effects and achieve more powerful results. This demonstrates the potential of our approach to capture intricate light transport phenomena and enhance the overall rendering capabilities.
C.4 Novel View Synthesis
In order to assess ReTR's performance in Novel View Synthesis, a task where many multi-view stereo techniques struggle, we conduct a quantitative comparison with VolRecon [8]. The novel views are generated during the full reconstruction for fusing the point clouds as we discussed in the main paper. Our results demonstrate a significant improvement over VolRecon in terms of novel view synthesis, as shown in Tab. 8. Additionally, we provide visualizations of novel view synthesis and depth synthesis in Figure 10. It has been challenging to achieve high-quality results simultaneously in rendering-based studies and reconstruction-based studies, with few methods excelling in both aspects. However, the results achieved by our proposed framework, ReTR, are highly promising, which suggests that a learnable rendering approach based on transformers can effectively integrate both tasks, yielding impressive results on both fronts within a unified framework.
Our method requires approximately 30 seconds to render a depth map and image with a resolution of  $600 \times 800$ . Similar to other rendering-based methods such as IBRNet [30], VolRecon [8], and MVSNeRF [31], our approach has limitations in terms of efficiency. While learning-based rendering offers enhanced capabilities, it does introduce additional training parameters compared to traditional volume rendering techniques. Stacking multiple layers of our model can improve performance; however, it also increases training time due to the larger model size. It is important to strike a balance between achieving higher rendering quality and maintaining reasonable computational efficiency. Further research and optimization efforts can be explored to enhance the efficiency of our method,
Error Bar of Ours (ReTR)
Figure 8: Error bar on 3 runs of Ours (ReTR).
potentially through techniques such as model compression, parallelization, or hardware acceleration. Acknowledging these limitations, we aim to provide a comprehensive understanding of the trade-offs between rendering quality, efficiency, and model complexity within our proposed framework.