GHPT/index.html at master · NWU-VISLAB/GHPT · GitHub

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              <!-- Paper title -->
              <h1 class="title is-1 publication-title">GHPT: Real-Time Relightable Gaussian Splatting using Hybrid Path
                Tracing</h1>
              <div class="is-size-5 publication-authors">
                <!-- TODO: Replace with your paper authors and their personal links -->
                <span class="author-block">
                  <a href="https://1benxiaohai1.github.io/about/" target="_blank">Jinyang Bo</a><sup>*</sup>,</span>
                <span class="author-block">
                  <a target="_blank">Fan Dou</a><sup>*</sup>,</span>
                <span class="author-block">
                  <a target="_blank">Wenrui Quan</a>,</span>
                <span class="author-block">
                  <a target="_blank">Shangxun Liu</a>,</span>
                <span class="author-block">
                  <a href="https://sapphiresoul.github.io/" target="_blank">Yang Xu</a><sup>†</sup>,</span>
                <span class="author-block">
                  <a target="_blank">Yuhe Zhang</a>,</span>
                <span class="author-block">
                  <a target="_blank">Kang Li</a>,</span>
                <span class="author-block">
                  <a target="_blank">Guohua Geng</a></span>
              </div>

              <div class="is-size-5 publication-authors">
                <!-- TODO: Replace with your institution and conference/journal info -->
                <span class="author-block">College of Computer Science, Northwest University, China<br>CVPR 2026</span>
                <!-- TODO: Remove this line if no equal contribution -->
                <span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution,<sup>†</sup>Indicates
                    Corresponding
                    author</small></span>

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                      class="external-link button is-normal is-rounded is-dark">
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                      <span>Paper</span>
                    </a>
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                      <span>Supplementary</span>
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    </section>


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          <h2 class="subtitle has-text-centered">
            Real-time Gaussian splatting relighting and composition
            under the City environment map. 113 fps at 1920×1080, NVIDIA
            GeForce RTX 4080.
          </h2>
        </div>
      </div>
    </section>
    <!-- End teaser image -->

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            <h2 class="title is-3">Abstract</h2>
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              <p>
                3D Gaussian splatting (3DGS) has emerged as a promising approach for high-fidelity 3D scene
                representation. However,relighting and composition of Gaussian splatting remain challenging because path
                tracing is not directly applicable. Existing relighting methods for Gaussian splatting typically adopt
                either approximate rendering formulations or rely on Gaussian ray tracing, yielding low relighting
                performance and low rendering efficiency. To address these limitations,we propose Gaussian hybrid path
                tracing (GHPT), a three stage framework to acquire relightable Gaussian splatting models. The first
                stage utilizes planar-based Gaussian splatting reconstruction representation (PGSR) to enable multi-view
                consistent depth rendering and reconstruct the surface mesh of a scene. The second stage performs
                physically-based differentiable rendering on the obtained mesh to reconstruct the material maps and the
                environment map. The third stage utilizes factorized inverse path tracing (FIPT) on the Gbuffer rendered
                by the PGSR, and visibility and indirect illu mination are evaluated by hardware-accelerated ray tracing
                on the mesh with the material maps and the environment map reconstructed in the second stage.
                Experiments demonstrate that the relighting performance of GHPT outperforms the baselines, and our
                method can perform real-time relighting and composition of Gaussian splatting.
              </p>
            </div>
          </div>
        </div>
      </div>
    </section>
    <!-- End paper abstract -->

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                <p>
                  Our three-stage GS-based inverse rendering pipeline using hybrid path tracing. In the first stage, the
                  scene geometry is
                  reconstructed from multi-view images through planar-based Gaussians guided by normal priors. The
                  second stage employs physically-based
                  differentiable rendering to recover material properties of the mesh and the environment map. In the
                  final stage, we use FIPT to recover
                  material property of each Gaussian and take into account visibility and indirect illumination by ray
                  tracing the underlying mesh.
                </p>
                <p></p>
              </div>
            </div>
          </div>
        </div>
      </div>

    </section>
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          <img src="./static/images/pipeline.png"
            alt="Our three-stage GS-based inverse rendering pipeline using hybrid path tracing" loading="lazy" />
          <h2 class="subtitle has-text-centered">
            <b>Three-stage GS-based inverse rendering pipeline.</b>
          </h2>
        </div>
      </div>
    </section>

    <!--Qualitative  Results -->
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                <p>
                  Qualitative comparison of relighting results under different environment maps on the Synthetic4Relight
                  and TensoIR Synthetic datasets.
                </p>
              </div>
            </div>
          </div>
        </div>
      </div>

    </section>
    <!-- End  Qualitative Results -->

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              <!-- TODO: Replace with your research result images -->
              <img src="static/images/Qualitative_results_on_Syn4Relight.png" alt="First research result visualization"
                loading="lazy" />
              <!-- TODO: Replace with description of this result -->
              <h2 class="subtitle has-text-centered">
                Qualitative comparison of relighting results under different environment maps on Synthetic4Relight
                dataset.
              </h2>
            </div>
            <div class="item">
              <!-- Your image here -->
              <img src="static/images/Qualitative_results_on_Tensoir.png" alt="Second research result visualization"
                loading="lazy" />
              <h2 class="subtitle has-text-centered">
                Qualitative comparison of relighting results under different environment maps on TensoIR Synthetic
                dataset.
              </h2>
            </div>
          </div>
        </div>
      </div>
    </section>
    <!--End Qualitative Results Images  -->

    <!-- Quantitative Results -->
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                <p>
                  Quantitative comparison on the Synthetic4Relight and TensoIR Synthetic datasets.
                </p>
              </div>
            </div>
          </div>
        </div>
      </div>

    </section>
    <!-- End Quantitative Results-->

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          <img src="./static/images/Quantitative_results.png" alt="Quantitative results on Synthetic4Relight dataset"
            loading="lazy" />
        </div>
      </div>
    </section>
    <!--End Quantitative Results Images -->

    <!-- Relighting and Composition Results -->
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                  Real-time relighting and composition results in the Garden, Kitchen, and Room scenes. From left to
                  right: results without path tracing, results of our method, and results of our method relit by two
                  environment maps, Bridge
                  and Fireplace. Our method can produce more realistic results due to the presence of shadows and
                  indirect illumination.
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            <b>Real-time relighting and composition results</b>
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        <pre id="bibtex-code"><code>@article{Bo2026GHPT,
  title={GHPT: Real-Time Relightable Gaussian Splatting using Hybrid Path Tracing},
  author={Bo, Jinyang and Dou, Fan and Quan, Wenrui and Liu, Shangxun and Xu, Yang and Zhang, Yuhe and Li, Kang and Geng,
  Guohua},
  Conference={IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2026}
}</code></pre>
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