max_radii2D does not seem to be work correctly.

[archershot]

gaussian-splatting/scene/gaussian_model.py

Lines 452 to 469 in 54c035f

	def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size, radii):
	grads = self.xyz_gradient_accum / self.denom
	grads[grads.isnan()] = 0.0
	self.tmp_radii = radii
	self.densify_and_clone(grads, max_grad, extent)
	self.densify_and_split(grads, max_grad, extent)
	prune_mask = (self.get_opacity < min_opacity).squeeze()
	if max_screen_size:
	big_points_vs = self.max_radii2D > max_screen_size
	big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent
	prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws)
	self.prune_points(prune_mask)
	tmp_radii = self.tmp_radii
	self.tmp_radii = None
	torch.cuda.empty_cache()

gaussian-splatting/scene/gaussian_model.py

Lines 388 to 407 in 54c035f

	def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_tmp_radii):
	d = {"xyz": new_xyz,
	"f_dc": new_features_dc,
	"f_rest": new_features_rest,
	"opacity": new_opacities,
	"scaling" : new_scaling,
	"rotation" : new_rotation}
	optimizable_tensors = self.cat_tensors_to_optimizer(d)
	self._xyz = optimizable_tensors["xyz"]
	self._features_dc = optimizable_tensors["f_dc"]
	self._features_rest = optimizable_tensors["f_rest"]
	self._opacity = optimizable_tensors["opacity"]
	self._scaling = optimizable_tensors["scaling"]
	self._rotation = optimizable_tensors["rotation"]
	self.tmp_radii = torch.cat((self.tmp_radii, new_tmp_radii))
	self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
	self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
	self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")

max_radii2D was used for gaussian prune. While it would be always set to zero before by densification_postfix of densify_and_clone/densify_and_split.