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REVIEW 4 major objections 5 minor 64 references

TeSO: Representing and Compressing 3D Point Cloud Scenes with Textured Surfel Octree

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A new 3D representation, the textured surfel octree, is claimed to deliver higher rendering quality at lower bit-rates than existing point-cloud and 3D-Gaussian pipelines.

desk verdict TeSO is a genuine new representation with measured R-D gains, but the Poisson-mesh ground truth and sole LPIPS metric keep me from fully trusting the headline numbers. read the letter →

arxiv 2508.07083 v1 pith:CTN7EQU6 submitted 2025-08-09 cs.CV

Yueyu Hu , Ran Gong , Tingyu Fan , Yao Wang This is my paper · ORCID
classification cs.CV
keywords 3Drepresentationsurfeloctreepointcloudcompressiontexturemappingrate-distortionGaussiansplattinglearnedentropymodelreal-timerendering
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes TeSO, a 3D representation that stores a scene's geometry as flat, cube-bounded surfels arranged on an octree, with a separate small texture patch attached to each surfel. The central claim is that by using large surfels for smooth regions and preserving fine detail in texture maps, TeSO can render high-quality views at lower bit-rates than point-cloud or 3D-Gaussian-based baselines, without any decoder-side surface reconstruction. The paper also introduces a compression scheme that codes the octree geometry losslessly with a learned entropy model and codes the texture patches using G-PCC or AV1. On the four-scene 8iVFB test set, TeSO is reported to achieve lower LPIPS than G-PCC+OpenGL, G-PCC+P2ENet, and B2P at matched bit-rates, at both 1024x1024 and 1920x1920 resolutions.

What carries the argument

The central object is the textured surfel octree: each cube-bounded surfel is a plane with a center offset, normal, and radius, clipped to its octree cube; a soft semi-transparent border extends beyond the cube and blends with neighbors to hide cracks. Each surfel carries a small M×M texture patch in its tangent frame, computed from the original point colors. The compression chain quantizes the geometry attributes, losslessly codes octree occupancy, leaf flags, and attributes with a learned sparse-convolutional entropy model conditioned on previously coded levels and attributes, then codes texture patches either as a packed image with AV1 or as point colors with G-PCC. This machinery keeps t

What would settle it

Render the same bitstreams for the 8iVFB scenes at 1920x1920 and run a human pairwise-preference study between TeSO and G-PCC+P2ENet at matched bpp; if viewers do not prefer TeSO, or if a different objective metric such as DISTS reverses the ordering, the claim of higher rendering quality at lower bit-rates would be refuted. Alternatively, re-run the R-D comparison with ground truth taken from a high-quality multi-view capture rather than Poisson reconstruction; if TeSO's advantage shrinks or reverses, the result depends on the ground-truth choice.

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Extended reading notes

Core claim

The paper's core claim is that decoupling geometry from texture in a hierarchical surfel representation improves the rate-distortion frontier for streaming 3D scenes. A scene is encoded as a set of cube-bounded planes — each with an offset, normal, and radius — stored on the leaves and selected internal nodes of an octree, with a small texture patch per surfel. Construction starts from a point cloud: points are grouped by cube at coarse levels, and a cube is kept as one surfel if a sampled grid on the surfel reconstructs the points with D1-PSNR above a threshold; otherwise the cube splits. On the 8iVFB benchmark, the compressed representation is reported to yield lower LPIPS at equal bits-pe

Load-bearing premise

The headline improvement is measured only with LPIPS against ground-truth views that are rasterizations of a Poisson surface reconstruction of the source point cloud; if LPIPS does not match human perception for these artifacts, or if Poisson reconstruction systematically favors smooth parametric surfaces, the reported rate-distortion advantage may not carry over to real captures or to human viewers.

Editorial extensions

If this is right

  • Directly renderable streaming: a receiver decodes TeSO into surfel primitives and rasters them immediately, eliminating the surface-reconstruction or learned-rendering step that point-cloud pipelines require.
  • Bit-rate can be spent on texture instead of geometry in smooth areas, so scenes with flat regions and high-frequency color keep their appearance even when geometry is aggressively simplified.
  • The same octree structure supports partial and viewport-adaptive streaming: texture patches are local, so a client can fetch only the surfels and patches that are visible.
  • The geometry bitstream is lossless once attributes are quantized, so coding distortion is controlled entirely by geometry quantization, splitting threshold, and texture quantization.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because TeSO rendering is differentiable, the hand-tuned splitting threshold and quantizer steps could be replaced by a learned rate-distortion optimization, a direction the paper names but does not pursue.
  • The ground-truth evaluation uses Poisson-reconstructed meshes, which are smooth; if real multi-view captures are used instead, the comparison with point-based renderers might shift, so the reported margin is likely tied to the smoothness of the reference.
  • The soft-blending width is fixed to the finest cube width; scenes mixing very different object scales might need an adaptive blending range, which the current method does not address.
  • Since texture coding via G-PCC outperforms AV1 image packing, a learned texture codec that respects the 3D adjacency of patches would likely improve the rate-distortion curve further.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes Textured Surfel Octree (TeSO), a 3D representation constructed from point clouds: an octree whose leaf nodes carry cube-bounded planar surfels, each with an attached texture patch. It describes a CUDA-accelerated construction algorithm, a rasterization-based renderer with soft blending, and a compression scheme that losslessly codes the octree geometry with a learned entropy model while coding texture either by packing patches into AV1 images or by rasterizing the TeSO into a colored point cloud and coding colors with G-PCC. The central claim is that TeSO plus this compression achieves lower LPIPS at equal or lower bit-rates than G-PCC+OpenGL, G-PCC+P2ENet, and B2P on the 8iVFB dataset, at both 1024x1024 and 1920x1920.

Significance. If the rate-distortion claim holds, TeSO is a practically interesting representation: it combines explicit surface geometry with high-frequency texture, is directly renderable without decoder-side surface reconstruction, and is compressible with a mixture of learned and standard codecs. The construction-speed and decoding-latency numbers are also useful engineering results, and the ablation in Table 1 gives some insight into the entropy model. However, the current evidence is not yet sufficient to establish the headline superiority: the evaluation rests on a single perceptual metric against a Poisson-derived ground truth, on only four sequences without variance reporting, and on baselines that include two of the authors' own prior systems.

major comments (4)
  1. [Sec. 6.1.1, Fig. 15] The R-D claim is measured only as LPIPS against views rasterized from a Poisson surface reconstruction of the source point cloud. TeSO is itself a smooth-surfel representation built from that same point cloud, so it may be systematically favored by this ground truth over point-splat and Gaussian baselines. The acknowledgment in Sec. 7 about LPIPS does not address this ground-truth confound. I request additional evidence: (i) a reference that is not derived from surface reconstruction (e.g., original multi-view captures if available, or a high-quality EWA/surfel rendering of the original point cloud), (ii) at least one additional distortion metric (PSNR/SSIM or a small subjective study), and (iii) per-scene R-D curves. Without this, the reported advantage could be an artifact of the evaluation protocol rather than a property of the representation.
  2. [Sec. 6.4 / Fig. 15] The headline comparison is averaged over only four 8iVFB sequences with no error bars or per-sequence breakdown. Two of the three baselines (P2ENet and B2P) are the authors' own prior methods, which increases the need for transparent reporting. Please provide per-sequence rates and distortions, standard deviations or confidence intervals, and the exact operating points that form the convex hulls. The introduction promises open-source code, but no code or data link is provided; releasing the implementation and bitstreams is important for independent verification of the R-D curves.
  3. [Sec. 5.3, 'Coding as Colored Point Cloud'] The description says that after the TeSO geometry is coded, 'we only need to transmit the bits for the color attributes in the point cloud bit-stream.' Since G-PCC TMC13 normally encodes geometry and attributes in a joint bitstream, it is not clear how attribute-only decoding with pre-existing geometry is performed. If a modified or non-default configuration is used, the exact software configuration and bitstream-size accounting must be described; otherwise the reported bpp for the G-PCC-texture variant may omit geometry-related overhead. Please clarify this step and, if possible, provide a bitrate breakdown by geometry and texture.
  4. [Sec. 6.1.2 / Fig. 15] The P2ENet baseline is reported as trained for 1024x1024 rendering, yet it is also evaluated at 1920x1920. If the learned renderer is resolution-dependent, this may disadvantage it at the higher resolution and inflate the apparent gap. Please state whether P2ENet was retrained or adapted for 1920x1920, or discuss its resolution generalization. The same level of detail should be provided for B2P's configuration and training data.
minor comments (5)
  1. [Sec. 3.3] The decision function f is described as comparing D1-PSNR between grid points on the surfel and the point set, but the number and placement of grid points are not specified. Please make this precise for reproducibility.
  2. [Eq. (4)] The notation Sgn is undefined; use sign(·) or define it at first use.
  3. [Table 2] Typo: 'geoemetry' should be 'geometry'. Also, the table reports decoding times but not the standard deviation or the exact machine configuration beyond CPU/GPU model.
  4. [References] References [49] and [50] appear to be the same paper with identical titles; one should be removed or corrected. Reference [1] is missing a closing period.
  5. [Fig. 15] The caption does not state how many R-D points are averaged, what the curve construction procedure is, or whether shaded regions (if any) denote variance. Please clarify.

Circularity Check

0 steps flagged · score 2.0 of 10

No derivation-level circularity: the rate-distortion claim is measured and anchored by an external standard; only minor non-load-bearing self-citations appear.

full rationale

The paper's central claim is an empirical rate-distortion comparison, not a derived identity. TeSO geometry is constructed from point clouds by a thresholded surfel-fitting algorithm; texture patches are resampled from the same points; compression uses a learned entropy model trained on a different dataset plus standard codecs (AV1, G-PCC). None of these steps defines the reported rendering quality in terms of a fitted parameter or a self-cited result. The two strongest non-standard baselines (B2P [20], P2ENet [19]) are the authors' own prior works, so the comparison setup could be biased if those baselines are under-tuned; however, the claim also includes G-PCC, an external MPEG standard, and the ranking is obtained by rendering and measuring LPIPS, not by construction. The Poisson-mesh ground truth and LPIPS-only evaluation are genuine threats to external validity (acknowledged in Sec. 7 for LPIPS), but they are evaluation confounds, not circular reductions. Accordingly, no circular step is identified; the score reflects one minor non-load-bearing self-citation cluster.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

TeSO introduces a data structure, not a physical entity. The 'soft area' blending and 'virtual children' context nodes are rendering and coding heuristics with in-paper motivation and visual evidence, not unobserved entities. The main load-bearing costs are the measurement assumptions (LPIPS, Poisson-mesh ground truth) and the hand-chosen construction parameters listed above.

free parameters (6)
  • D1-PSNR split threshold tau = {60, 62, 64, 66}
    Controls octree leaf granularity and is swept to produce R-D operating points; a hand-chosen fidelity knob, not fitted to the test set, but it defines what 'smooth enough' means.
  • Texture patch resolution M per level = (12, 8, 4) for levels (6, 7, 8)
    Hand-chosen texture grid sizes that directly control texture bit cost and sharpness (Sec. 3.4).
  • Quantization steps for offset, normal, radius = 0.5, 1/64, 1/16
    Asserted to not affect rendering quality compared to the original attributes, but no supporting measurement is shown (Sec. 5).
  • Nearest-neighbor count K for texture interpolation = 3
    Chosen for texture patch filling (Sec. 3.4); not justified experimentally.
  • Soft-area blending sigma = width of the smallest bounding cube
    Hyperparameter for crack avoidance; set to the finest octree cube width (Sec. 3.2).
  • Octree levels range = lmin=6, lmax=8
    Design choice for 10-bit coordinate point clouds; bounds the granularity range of the representation.
assumptions (4)
  • domain assumption Input point clouds have reliable per-point normals and colors (normals estimated via Hoppe et al. when absent).
    Surfel planes, texture coordinate frames, and texture patches are all derived from these attributes (Sec. 3.3 and 3.4).
  • domain assumption LPIPS against Poisson-mesh-rasterized views is a valid perceptual rendering-quality metric for this comparison.
    The entire R-D evaluation rests on this metric; the paper itself states LPIPS alignment with human perception is assumed (Sec. 6.1.1 and Conclusion).
  • domain assumption A single plane with averaged normal and max radius is a sufficient local surface model when D1-PSNR exceeds tau on sampled grid points.
    This is the construction criterion f() in Sec. 3.3; it holds only for solid, opaque surfaces, a limitation the authors acknowledge in the Conclusion.
  • domain assumption Poisson surface reconstruction of the source point cloud yields the correct appearance ground truth.
    Ground-truth views are rasterizations of the Poisson mesh (Sec. 6.1.1); any bias in Poisson reconstruction transfers into the metric.

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Cite this review

Pith. "Pith review of TeSO: Representing and Compressing 3D Point Cloud Scenes with Textured Surfel Octree." pith.science (2026). https://pith.science/paper/CTN7EQU6

@misc{pith2026250807083,
  author       = {Pith},
  title        = {Pith review of: TeSO: Representing and Compressing 3D Point Cloud Scenes with Textured Surfel Octree},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CTN7EQU6}},
  note         = {Machine review of arXiv:2508.07083}
}
read the original abstract

3D visual content streaming is a key technology for emerging 3D telepresence and AR/VR applications. One fundamental element underlying the technology is a versatile 3D representation that is capable of producing high-quality renders and can be efficiently compressed at the same time. Existing 3D representations like point clouds, meshes and 3D Gaussians each have limitations in terms of rendering quality, surface definition, and compressibility. In this paper, we present the Textured Surfel Octree (TeSO), a novel 3D representation that is built from point clouds but addresses the aforementioned limitations. It represents a 3D scene as cube-bounded surfels organized on an octree, where each surfel is further associated with a texture patch. By approximating a smooth surface with a large surfel at a coarser level of the octree, it reduces the number of primitives required to represent the 3D scene, and yet retains the high-frequency texture details through the texture map attached to each surfel. We further propose a compression scheme to encode the geometry and texture efficiently, leveraging the octree structure. The proposed textured surfel octree combined with the compression scheme achieves higher rendering quality at lower bit-rates compared to multiple point cloud and 3D Gaussian-based baselines.

Figures

Figures reproduced from arXiv: 2508.07083 by the authors.

Figure 1
Figure 1. Comparison of the proposed TeSO in a 3D streaming [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of geometry and texture complexity. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. 2D illustration of the structure of the surfel octree. When approximating an underlying surface (solid curve in red), [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Illustration of the soft area extending outside the [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Derivation of 𝑛® and 𝑟 for the surfel. (a) The average surface normal is calculated from the normals of all points in the cube. (b) The radius 𝑟 is calculated as the maximum distance from the center of the surfel (namely 𝑃) to all points in the cube. It then calculates…
Figure 6
Figure 6. Figure 6: Illustration of the tangent space coordinate system and the texture representation on the surfels. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 9
Figure 9. Figure 9: Illustration of context preparation involving neigh [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]
Figure 8
Figure 8. Figure 8: Coding order and conditioning dependency of ge [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 11
Figure 11. Figure 11: The compression pipeline of the surfel octree geometry attributes with the rasterized context. [PITH_FULL_IMAGE:figures/full_fig_p008_11.png]
Figure 12
Figure 12. Figure 12: Packed texture patches at two different levels. [PITH_FULL_IMAGE:figures/full_fig_p008_12.png]
Figure 14
Figure 14. Figure 14: (a), (b) and (c) are rendering results of TeSO built from the original point clouds ( [PITH_FULL_IMAGE:figures/full_fig_p010_14.png]
Figure 15
Figure 15. Figure 15: Rate-distortion performance of the proposed method against G-PCC and B2P, under different resolutions. Labels [PITH_FULL_IMAGE:figures/full_fig_p011_15.png]
Figure 16
Figure 16. Figure 16: Rendering results of the compressed longdress scene at resolution 1920 × 1920. The G-PCC decoded point clouds rendered by OpenGL has visible gaps. P2ENet achieves better quality but still has gap issues at high resolutions. B2P renders smooth results but suffers from …
Figure 17
Figure 17. Figure 17: Rendering results of the redandblack. TeSO 0.55 bpp G-PCC (r04) + OpenGL 0.55 bpp [PITH_FULL_IMAGE:figures/full_fig_p013_17.png]
Figure 18
Figure 18. Figure 18: Rendering results of the Loot. TeSO 0.82 bpp G-PCC (r04) + OpenGL 0.89 bpp [PITH_FULL_IMAGE:figures/full_fig_p013_18.png]
Figure 19
Figure 19. Figure 19: Rendering results of the soldier. [20] Yueyu Hu, Ran Gong, and Yao Wang. 2024. Bits-to-photon: End-to-end learned scalable point cloud compression for direct rendering. arXiv preprint arXiv:2406.05915 (2024). [21] Lila Huang, Shenlong Wang, Kelvin Wong, Jerry Liu, and…

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.