Method forward

src/sharp/utils/gsplat.py:72–149

Predict images from gaussians. Args: gaussians: The Gaussians to render. extrinsics: The extrinsics of the camera to render to in OpenCV format. intrinsics: The intriniscs of the camera to render to in OpenCV format. image_width: The desired o

(
        self,
        gaussians: Gaussians3D,
        extrinsics: torch.Tensor,
        intrinsics: torch.Tensor,
        image_width: int,
        image_height: int,
    )

        self.low_pass_filter_eps = low_pass_filter_eps

    def forward(
        self,
        gaussians: Gaussians3D,
        extrinsics: torch.Tensor,
        intrinsics: torch.Tensor,
        image_width: int,
        image_height: int,
    ) -> RenderingOutputs:
        """Predict images from gaussians.

        Args:
            gaussians: The Gaussians to render.
            extrinsics: The extrinsics of the camera to render to in OpenCV format.
            intrinsics: The intriniscs of the camera to render to in OpenCV format.
            image_width: The desired output image width.
            image_height: The desired output image height.
        """
        batch_size = len(gaussians.mean_vectors)
        outputs_list: list[RenderingOutputs] = []

        for ib in range(batch_size):
            colors, alphas, meta = gsplat.rendering.rasterization(
                means=gaussians.mean_vectors[ib],
                quats=gaussians.quaternions[ib],
                scales=gaussians.singular_values[ib],
                opacities=gaussians.opacities[ib],
                colors=gaussians.colors[ib],
                viewmats=extrinsics[ib : ib + 1],
                Ks=intrinsics[ib : ib + 1, :3, :3],
                width=image_width,
                height=image_height,
                render_mode="RGB+D",
                rasterize_mode="classic",
                absgrad=False,
                packed=False,
                eps2d=self.low_pass_filter_eps,
            )

            rendered_color = colors[..., 0:3].permute([0, 3, 1, 2])
            rendered_depth_unnormalized = colors[..., 3:4].permute([0, 3, 1, 2])
            rendered_alpha = alphas.permute([0, 3, 1, 2])

            # Compose with background color.
            rendered_color = self.compose_with_background(
                rendered_color, rendered_alpha, self.background_color
            )

            # Colorspace conversion.
            if self.color_space == "sRGB":
                pass
            elif self.color_space == "linearRGB":
                rendered_color = cs_utils.linearRGB2sRGB(rendered_color)
            else:
                ValueError("Unsupported ColorSpace type.")

            # splats: (B, N, 10)
            cov2d = self._conics_to_covars2d(meta["conics"])
            # Set the cov2d of invisible splats to 1 to avoid nan in condition number calculation..