Critical Fixes:
- Security: Add weights_only=True to torch.load (with fallback for older PyTorch)
- Thread Safety: Fix bare except clauses, use specific exceptions (Full, Empty)
- Thread Safety: Add shutdown flag and locks for async colorization
- Resource Management: Add explicit cleanup() methods with proper shutdown
- Input Validation: Comprehensive validation for all user inputs

Error Handling Improvements:
- da3_inference_optimized.py:
  * Validate image inputs (size, dimensions, NaN/inf values)
  * Validate model predictions before processing
  * Safe torch.load with weights_only parameter
  * Warning when TensorRT doesn't support confidence output
  * Proper cleanup method for GPU resources

- depth_anything_3_node_optimized.py:
  * Replace bare except with specific exceptions (queue.Full)
  * Add thread shutdown flag and synchronization
  * Validate images after conversion
  * Deep copy camera_info to avoid modifying shared message
  * Improved thread cleanup with longer timeout
  * Explicit model cleanup call

- gpu_utils.py:
  * Validate tensor/array inputs (None, empty, NaN values)
  * Fix hardcoded GPU device index (use current_device)
  * Fix dtype handling in pinned_numpy_array
  * Add cleanup method to CUDAStreamManager
  * Comprehensive input validation for upsample operations

- convert_to_tensorrt.py:
  * Validate input size arguments
  * Check output path writeability early
  * Handle file I/O errors gracefully
  * Fix division by zero in speedup calculation
  * Add GPU memory cleanup after conversion

Resource Management:
- Added explicit cleanup() methods to all classes
- Fixed __del__ to not raise exceptions
- Proper thread shutdown with flags and timeouts
- GPU memory cleanup in all exit paths
- CUDA stream cleanup method

Thread Safety:
- Added _running flag for thread coordination
- Added _shutdown_lock for publisher access
- Fixed race conditions in async colorization
- Proper thread join with configurable timeout (5s)
- Clear queue on shutdown

Validation:
- Check for None, empty, and invalid inputs
- Validate array/tensor dimensions
- Check for NaN and infinite values
- Validate model predictions structure
- Range checks for all numeric parameters

These fixes address all critical and high-severity issues identified in code review,
ensuring robust operation under edge cases and proper resource cleanup.